Perception Benchmark Live Viewer
-
+
Connecting...
- 
+
`;
// Also get embryoId and groundTruth from stored data if not provided
embryoId = embryoId || imgData.embryoId;
@@ -628,10 +630,13 @@ async def broadcast(message: Dict):
// Update header
document.querySelector('.trace-section h2').innerHTML =
- `Reasoning Trace - T${timepoint} `;
+ `Reasoning Trace - T${timepoint} `;
if (traceSteps.length === 0) {
- list.innerHTML = 'THREE-VIEW (XY | YZ / XZ)
-
+
+ THREE-VIEW (XY | YZ / XZ)
+
No trace recorded for T' + timepoint + '
';
+ list.innerHTML = 'No trace recorded for T'
+ + timepoint + '
';
return;
}
@@ -685,12 +690,16 @@ async def broadcast(message: Dict):
'verified' : '';
html += `
-
+
`;
}
@@ -749,10 +758,14 @@ async def websocket_endpoint(websocket: WebSocket):
connected_clients.append(websocket)
# Send current state
- await websocket.send_text(json.dumps({
- "type": "status",
- "status": benchmark_state["status"],
- }))
+ await websocket.send_text(
+ json.dumps(
+ {
+ "type": "status",
+ "status": benchmark_state["status"],
+ }
+ )
+ )
try:
while True:
@@ -789,8 +802,8 @@ def __init__(
embryo_id: str,
enable_verification: bool = True,
start_timepoint: int = 0,
- max_timepoints: Optional[int] = None,
- trace_dir: Optional[Path] = None,
+ max_timepoints: int | None = None,
+ trace_dir: Path | None = None,
):
self.testset = testset
self.embryo_id = embryo_id
@@ -798,7 +811,7 @@ def __init__(
self.start_timepoint = start_timepoint
self.max_timepoints = max_timepoints
- self.predictions: List[Dict] = []
+ self.predictions: list[dict] = []
self.correct_count = 0
self.adjacent_count = 0
self.verified_count = 0
@@ -808,7 +821,7 @@ def __init__(
self.trace_dir = trace_dir or Path("benchmarks/results/traces")
self.run_dir = self.trace_dir / f"{self.run_id}_{embryo_id}"
self.run_dir.mkdir(parents=True, exist_ok=True)
- self.traces: Dict[int, List[Dict]] = {} # timepoint -> trace steps
+ self.traces: dict[int, list[dict]] = {} # timepoint -> trace steps
logger.info(f"Trace persistence enabled: {self.run_dir}")
@@ -861,23 +874,23 @@ async def run(self):
benchmark_state["current_timepoint"] = test_case.timepoint
# Send image(s)
- await broadcast({
- "type": "image",
- "embryo_id": self.embryo_id,
- "timepoint": test_case.timepoint,
- "ground_truth": test_case.ground_truth_stage,
- "image": test_case.image_b64, # Combined for backward compat
- "top_image": test_case.top_image_b64,
- "side_image": test_case.side_image_b64,
- })
+ await broadcast(
+ {
+ "type": "image",
+ "embryo_id": self.embryo_id,
+ "timepoint": test_case.timepoint,
+ "ground_truth": test_case.ground_truth_stage,
+ "image": test_case.image_b64, # Combined for backward compat
+ "top_image": test_case.top_image_b64,
+ "side_image": test_case.side_image_b64,
+ }
+ )
# Clear trace for new prediction
await broadcast({"type": "clear_trace"})
# Run perception with trace streaming
- result = await self._run_perception_with_streaming(
- engine, session, test_case
- )
+ result = await self._run_perception_with_streaming(engine, session, test_case)
# Check accuracy
is_correct = result.stage == test_case.ground_truth_stage
@@ -915,23 +928,29 @@ async def run(self):
await broadcast(pred_msg)
# Save trace for this timepoint
- self._save_timepoint_trace(test_case.timepoint, result, test_case, is_correct, is_adjacent)
+ self._save_timepoint_trace(
+ test_case.timepoint, result, test_case, is_correct, is_adjacent
+ )
# Send updated stats
total = len(self.predictions)
- await broadcast({
- "type": "stats",
- "accuracy": self.correct_count / total if total > 0 else None,
- "adjacent": self.adjacent_count / total if total > 0 else None,
- "total": total,
- "verified": self.verified_count,
- })
+ await broadcast(
+ {
+ "type": "stats",
+ "accuracy": self.correct_count / total if total > 0 else None,
+ "adjacent": self.adjacent_count / total if total > 0 else None,
+ "total": total,
+ "verified": self.verified_count,
+ }
+ )
# Add observation to session with simulated timestamp
# Typical diSPIM acquisition interval is ~4 minutes per timepoint
- simulated_timestamp = datetime.now() - timedelta(
- minutes=(self.max_timepoints or 100) * 4
- ) + timedelta(minutes=test_case.timepoint * 4)
+ simulated_timestamp = (
+ datetime.now()
+ - timedelta(minutes=(self.max_timepoints or 100) * 4)
+ + timedelta(minutes=test_case.timepoint * 4)
+ )
session.add_observation(
timepoint=test_case.timepoint,
@@ -953,7 +972,6 @@ async def run(self):
async def _run_perception_with_streaming(self, engine, session, test_case):
"""Run perception and stream trace steps."""
- from gently.harness.perception.session import ReasoningStep
# We need to hook into the reasoning trace
# For now, run perception and stream the trace after
@@ -985,7 +1003,9 @@ async def _run_perception_with_streaming(self, engine, session, test_case):
return result
- def _save_timepoint_trace(self, timepoint: int, result, test_case, is_correct: bool, is_adjacent: bool):
+ def _save_timepoint_trace(
+ self, timepoint: int, result, test_case, is_correct: bool, is_adjacent: bool
+ ):
"""Save trace for a single timepoint to disk."""
trace_data = {
"timepoint": timepoint,
@@ -1035,7 +1055,7 @@ async def run_benchmark_background(
embryo_id: str,
enable_verification: bool,
start_timepoint: int = 0,
- max_timepoints: Optional[int] = None,
+ max_timepoints: int | None = None,
):
"""Run benchmark in background after server starts."""
print("[DEBUG] run_benchmark_background starting", flush=True)
@@ -1049,7 +1069,10 @@ async def run_benchmark_background(
# Load data
ground_truth = GroundTruth.from_json(ground_truth_path)
- print(f"[DEBUG] Loaded ground truth: {len(ground_truth.transitions)} embryos", flush=True)
+ print(
+ f"[DEBUG] Loaded ground truth: {len(ground_truth.transitions)} embryos",
+ flush=True,
+ )
testset = OfflineTestset(
session_path=session_path,
ground_truth=ground_truth,
@@ -1071,6 +1094,7 @@ async def run_benchmark_background(
except Exception as e:
print(f"[DEBUG] ERROR in run_benchmark_background: {e}", flush=True)
import traceback
+
traceback.print_exc()
@@ -1142,9 +1166,9 @@ def main():
trace_dir = Path("benchmarks/results/traces")
run_id = datetime.now().strftime("%Y%m%d_%H%M%S")
- print(f"\n{'='*60}")
+ print(f"\n{'=' * 60}")
print("Perception Benchmark Live Viewer")
- print(f"{'='*60}")
+ print(f"{'=' * 60}")
print(f"Session: {session_path}")
print(f"Embryo: {args.embryo}")
print(f"Start timepoint: T{args.start_timepoint}")
@@ -1152,7 +1176,7 @@ def main():
print(f"Verification: {'disabled' if args.no_verification else 'enabled'}")
print(f"Traces: {trace_dir / f'{run_id}_{args.embryo}'}")
print(f"URL: http://localhost:{args.port}")
- print(f"{'='*60}\n")
+ print(f"{'=' * 60}\n")
# Open browser
if not args.no_browser:
@@ -1162,14 +1186,16 @@ def main():
@app.on_event("startup")
async def startup_event():
print("[DEBUG] Startup event fired", flush=True)
- asyncio.create_task(run_benchmark_background(
- session_path=session_path,
- ground_truth_path=gt_path,
- embryo_id=args.embryo,
- enable_verification=not args.no_verification,
- start_timepoint=args.start_timepoint,
- max_timepoints=args.max_timepoints,
- ))
+ asyncio.create_task(
+ run_benchmark_background(
+ session_path=session_path,
+ ground_truth_path=gt_path,
+ embryo_id=args.embryo,
+ enable_verification=not args.no_verification,
+ start_timepoint=args.start_timepoint,
+ max_timepoints=args.max_timepoints,
+ )
+ )
print("[DEBUG] Background task created", flush=True)
# Run server
diff --git a/benchmarks/perception/metrics.py b/benchmarks/perception/metrics.py
index f69c054f..97d5c0f9 100644
--- a/benchmarks/perception/metrics.py
+++ b/benchmarks/perception/metrics.py
@@ -6,14 +6,23 @@
from collections import defaultdict
from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from .runner import BenchmarkReport
# Stage order for metrics
-STAGE_ORDER = ["early", "bean", "comma", "1.5fold", "2fold", "pretzel", "hatching", "hatched"]
+STAGE_ORDER = [
+ "early",
+ "bean",
+ "comma",
+ "1.5fold",
+ "2fold",
+ "pretzel",
+ "hatching",
+ "hatched",
+]
@dataclass
@@ -25,30 +34,30 @@ class PerceptionMetrics:
adjacent_accuracy: float = 0.0 # Within 1 stage
# Per-stage accuracy
- stage_accuracy: Dict[str, float] = field(default_factory=dict)
- stage_counts: Dict[str, int] = field(default_factory=dict)
+ stage_accuracy: dict[str, float] = field(default_factory=dict)
+ stage_counts: dict[str, int] = field(default_factory=dict)
# Confusion matrix: confusion[gt_stage][pred_stage] = count
- confusion_matrix: Dict[str, Dict[str, int]] = field(default_factory=dict)
+ confusion_matrix: dict[str, dict[str, int]] = field(default_factory=dict)
# Confidence calibration
mean_confidence: float = 0.0
confidence_when_correct: float = 0.0
confidence_when_wrong: float = 0.0
- calibration_bins: List[Tuple[float, float, int]] = field(default_factory=list)
+ calibration_bins: list[tuple[float, float, int]] = field(default_factory=list)
# (confidence_bin_center, accuracy_in_bin, count)
expected_calibration_error: float = 0.0 # ECE
# Temporal metrics
backward_transitions: int = 0 # Errors where stage went backward
- stage_transition_delay: Dict[str, float] = field(default_factory=dict)
+ stage_transition_delay: dict[str, float] = field(default_factory=dict)
# How many timepoints after GT transition until prediction caught up
# Tool usage
total_tool_calls: int = 0
tool_call_rate: float = 0.0 # Avg tool calls per prediction
- tool_use_by_stage: Dict[str, float] = field(default_factory=dict)
+ tool_use_by_stage: dict[str, float] = field(default_factory=dict)
# When tools were used vs not
accuracy_with_tools: float = 0.0
@@ -59,7 +68,7 @@ class PerceptionMetrics:
transitional_rate: float = 0.0
transitional_accuracy: float = 0.0 # Accuracy when marked transitional
- def to_dict(self) -> Dict[str, Any]:
+ def to_dict(self) -> dict[str, Any]:
return {
"accuracy": self.accuracy,
"adjacent_accuracy": self.adjacent_accuracy,
@@ -102,8 +111,7 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
# Collect all predictions
all_preds = [
- p for r in report.embryo_results for p in r.predictions
- if p.ground_truth_stage is not None
+ p for r in report.embryo_results for p in r.predictions if p.ground_truth_stage is not None
]
if not all_preds:
@@ -116,8 +124,8 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
metrics.adjacent_accuracy = adjacent_correct / len(all_preds)
# Per-stage accuracy
- stage_correct = defaultdict(int)
- stage_total = defaultdict(int)
+ stage_correct: dict = defaultdict(int)
+ stage_total: dict = defaultdict(int)
for p in all_preds:
gt = p.ground_truth_stage
@@ -130,13 +138,11 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
metrics.stage_accuracy[stage] = stage_correct[stage] / stage_total[stage]
# Confusion matrix
- confusion = defaultdict(lambda: defaultdict(int))
+ confusion: dict = defaultdict(lambda: defaultdict(int))
for p in all_preds:
confusion[p.ground_truth_stage][p.predicted_stage] += 1
- metrics.confusion_matrix = {
- gt: dict(preds) for gt, preds in confusion.items()
- }
+ metrics.confusion_matrix = {gt: dict(preds) for gt, preds in confusion.items()}
# Confidence statistics
confidences = [p.confidence for p in all_preds]
@@ -156,10 +162,7 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
bin_high = (i + 1) / num_bins
bin_center = (bin_low + bin_high) / 2
- bin_preds = [
- p for p in all_preds
- if bin_low <= p.confidence < bin_high
- ]
+ bin_preds = [p for p in all_preds if bin_low <= p.confidence < bin_high]
if bin_preds:
bin_accuracy = sum(1 for p in bin_preds if p.is_correct) / len(bin_preds)
@@ -198,6 +201,8 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
stage_tool_calls[p.ground_truth_stage].append(p.tool_calls)
for stage, calls in stage_tool_calls.items():
+ if stage is None:
+ continue
metrics.tool_use_by_stage[stage] = sum(calls) / len(calls)
# Accuracy with vs without tools
@@ -207,7 +212,9 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
if with_tools:
metrics.accuracy_with_tools = sum(1 for p in with_tools if p.is_correct) / len(with_tools)
if without_tools:
- metrics.accuracy_without_tools = sum(1 for p in without_tools if p.is_correct) / len(without_tools)
+ metrics.accuracy_without_tools = sum(1 for p in without_tools if p.is_correct) / len(
+ without_tools
+ )
# Transitional observations
transitional_preds = [p for p in all_preds if p.is_transitional]
@@ -215,16 +222,16 @@ def compute_metrics(report: "BenchmarkReport") -> PerceptionMetrics:
metrics.transitional_rate = len(transitional_preds) / len(all_preds)
if transitional_preds:
- metrics.transitional_accuracy = sum(
- 1 for p in transitional_preds if p.is_correct
- ) / len(transitional_preds)
+ metrics.transitional_accuracy = sum(1 for p in transitional_preds if p.is_correct) / len(
+ transitional_preds
+ )
return metrics
def format_confusion_matrix(
- confusion: Dict[str, Dict[str, int]],
- stages: Optional[List[str]] = None,
+ confusion: dict[str, dict[str, int]],
+ stages: list[str] | None = None,
) -> str:
"""Format confusion matrix as ASCII table."""
if stages is None:
@@ -278,34 +285,38 @@ def format_metrics_summary(metrics: PerceptionMetrics) -> str:
count = metrics.stage_counts[stage]
lines.append(f" {stage:>10}: {acc:.1%} (n={count})")
- lines.extend([
- "",
- "CONFIDENCE CALIBRATION",
- f" Mean confidence: {metrics.mean_confidence:.2f}",
- f" Confidence (correct): {metrics.confidence_when_correct:.2f}",
- f" Confidence (wrong): {metrics.confidence_when_wrong:.2f}",
- f" Expected Cal. Error: {metrics.expected_calibration_error:.3f}",
- "",
- "TOOL USAGE",
- f" Total tool calls: {metrics.total_tool_calls}",
- f" Avg calls per pred: {metrics.tool_call_rate:.2f}",
- f" Accuracy with tools: {metrics.accuracy_with_tools:.1%}",
- f" Accuracy without tools: {metrics.accuracy_without_tools:.1%}",
- "",
- "TEMPORAL",
- f" Backward transitions: {metrics.backward_transitions}",
- "",
- "TRANSITIONAL OBSERVATIONS",
- f" Count: {metrics.transitional_count}",
- f" Rate: {metrics.transitional_rate:.1%}",
- f" Accuracy: {metrics.transitional_accuracy:.1%}",
- ])
+ lines.extend(
+ [
+ "",
+ "CONFIDENCE CALIBRATION",
+ f" Mean confidence: {metrics.mean_confidence:.2f}",
+ f" Confidence (correct): {metrics.confidence_when_correct:.2f}",
+ f" Confidence (wrong): {metrics.confidence_when_wrong:.2f}",
+ f" Expected Cal. Error: {metrics.expected_calibration_error:.3f}",
+ "",
+ "TOOL USAGE",
+ f" Total tool calls: {metrics.total_tool_calls}",
+ f" Avg calls per pred: {metrics.tool_call_rate:.2f}",
+ f" Accuracy with tools: {metrics.accuracy_with_tools:.1%}",
+ f" Accuracy without tools: {metrics.accuracy_without_tools:.1%}",
+ "",
+ "TEMPORAL",
+ f" Backward transitions: {metrics.backward_transitions}",
+ "",
+ "TRANSITIONAL OBSERVATIONS",
+ f" Count: {metrics.transitional_count}",
+ f" Rate: {metrics.transitional_rate:.1%}",
+ f" Accuracy: {metrics.transitional_accuracy:.1%}",
+ ]
+ )
if metrics.confusion_matrix:
- lines.extend([
- "",
- "CONFUSION MATRIX",
- format_confusion_matrix(metrics.confusion_matrix),
- ])
+ lines.extend(
+ [
+ "",
+ "CONFUSION MATRIX",
+ format_confusion_matrix(metrics.confusion_matrix),
+ ]
+ )
return "\n".join(lines)
diff --git a/benchmarks/perception/runner.py b/benchmarks/perception/runner.py
index 33d93fe3..70ac32fd 100644
--- a/benchmarks/perception/runner.py
+++ b/benchmarks/perception/runner.py
@@ -12,11 +12,11 @@
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any
from .ground_truth import GroundTruth
-from .testset import OfflineTestset, TestCase
from .metrics import PerceptionMetrics, compute_metrics
+from .testset import OfflineTestset
logger = logging.getLogger(__name__)
@@ -39,20 +39,20 @@ class BenchmarkConfig:
# Test settings
start_timepoint: int = 0
- max_timepoints_per_embryo: Optional[int] = None
- embryo_ids: Optional[List[str]] = None # None = all
+ max_timepoints_per_embryo: int | None = None
+ embryo_ids: list[str] | None = None # None = all
# Ablation toggles
include_temporal_context: bool = True
include_previous_observations: bool = True
# Custom system prompt override
- system_prompt_override: Optional[str] = None
+ system_prompt_override: str | None = None
# Metadata
description: str = ""
- def to_dict(self) -> Dict[str, Any]:
+ def to_dict(self) -> dict[str, Any]:
return {
"model": self.model,
"temperature": self.temperature,
@@ -78,20 +78,20 @@ class PredictionResult:
timepoint: int
predicted_stage: str
- ground_truth_stage: Optional[str]
+ ground_truth_stage: str | None
confidence: float
is_transitional: bool
- transition_between: Optional[List[str]]
+ transition_between: list[str] | None
reasoning: str
- reasoning_trace: Optional[Dict[str, Any]] # Serialized ReasoningTrace
+ reasoning_trace: dict[str, Any] | None # Serialized ReasoningTrace
tool_calls: int
- tools_used: List[str]
+ tools_used: list[str]
# Multi-phase verification fields
verification_triggered: bool = False
phase_count: int = 1
- verification_result: Optional[Dict[str, Any]] = None
- candidate_stages: Optional[List[Dict[str, Any]]] = None
+ verification_result: dict[str, Any] | None = None
+ candidate_stages: list[dict[str, Any]] | None = None
@property
def is_correct(self) -> bool:
@@ -113,7 +113,7 @@ def is_adjacent_correct(self) -> bool:
except ValueError:
return False
- def to_dict(self) -> Dict[str, Any]:
+ def to_dict(self) -> dict[str, Any]:
return {
"timepoint": self.timepoint,
"predicted_stage": self.predicted_stage,
@@ -139,9 +139,9 @@ class EmbryoResult:
"""Results for a single embryo run."""
embryo_id: str
- predictions: List[PredictionResult] = field(default_factory=list)
+ predictions: list[PredictionResult] = field(default_factory=list)
duration_seconds: float = 0.0
- error: Optional[str] = None
+ error: str | None = None
@property
def accuracy(self) -> float:
@@ -159,7 +159,7 @@ def adjacent_accuracy(self) -> float:
correct = sum(1 for p in self.predictions if p.is_adjacent_correct)
return correct / len(self.predictions)
- def to_dict(self) -> Dict[str, Any]:
+ def to_dict(self) -> dict[str, Any]:
return {
"embryo_id": self.embryo_id,
"predictions": [p.to_dict() for p in self.predictions],
@@ -175,11 +175,11 @@ class BenchmarkReport:
"""Complete benchmark report."""
config: BenchmarkConfig
- embryo_results: List[EmbryoResult] = field(default_factory=list)
- metrics: Optional[PerceptionMetrics] = None
+ embryo_results: list[EmbryoResult] = field(default_factory=list)
+ metrics: PerceptionMetrics | None = None
started_at: datetime = field(default_factory=datetime.now)
- completed_at: Optional[datetime] = None
- session_id: Optional[str] = None
+ completed_at: datetime | None = None
+ session_id: str | None = None
@property
def total_predictions(self) -> int:
@@ -192,7 +192,7 @@ def overall_accuracy(self) -> float:
return 0.0
return sum(1 for p in all_preds if p.is_correct) / len(all_preds)
- def to_dict(self) -> Dict[str, Any]:
+ def to_dict(self) -> dict[str, Any]:
return {
"config": self.config.to_dict(),
"embryo_results": [r.to_dict() for r in self.embryo_results],
@@ -222,7 +222,7 @@ def __init__(
self,
testset: OfflineTestset,
config: BenchmarkConfig,
- engine: Optional[Any] = None, # PerceptionEngine
+ engine: Any | None = None, # PerceptionEngine
):
"""
Parameters
@@ -246,7 +246,6 @@ async def _get_engine(self):
# Lazy import to avoid circular dependencies
import anthropic
from gently.harness.perception.engine import PerceptionEngine
- from gently.harness.perception.example_store import ExampleStore
client = anthropic.Anthropic()
@@ -465,7 +464,8 @@ async def main():
help="Description for this benchmark run",
)
parser.add_argument(
- "-v", "--verbose",
+ "-v",
+ "--verbose",
action="store_true",
help="Verbose logging",
)
@@ -481,6 +481,7 @@ async def main():
# The perception engine reads stage definitions etc. from the active
# organism module, which is normally loaded by launch_gently.py.
from gently.organisms import load_organism
+
load_organism("celegans")
# Find session path
diff --git a/benchmarks/perception/testset.py b/benchmarks/perception/testset.py
index 658cc011..c3514ffe 100644
--- a/benchmarks/perception/testset.py
+++ b/benchmarks/perception/testset.py
@@ -6,18 +6,19 @@
import base64
import io
+from collections.abc import Iterator
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
-from typing import Iterator, List, Optional, Tuple, Dict
+from typing import Any
import numpy as np
from .ground_truth import GroundTruth
# Lazy imports
-tifffile = None
-PIL_Image = None
+tifffile: Any = None
+PIL_Image: Any = None
def _ensure_dependencies():
@@ -26,10 +27,12 @@ def _ensure_dependencies():
if tifffile is None:
import tifffile as _tifffile
+
tifffile = _tifffile
if PIL_Image is None:
from PIL import Image as _Image
+
PIL_Image = _Image
@@ -40,27 +43,29 @@ class TestCase:
embryo_id: str
timepoint: int
image_b64: str # Combined view (for backward compatibility)
- top_image_b64: Optional[str] # TOP view only
- side_image_b64: Optional[str] # SIDE view only
- volume: Optional[np.ndarray]
- ground_truth_stage: Optional[str]
- acquired_at: Optional[datetime] = None
+ top_image_b64: str | None # TOP view only
+ side_image_b64: str | None # SIDE view only
+ volume: np.ndarray | None
+ ground_truth_stage: str | None
+ acquired_at: datetime | None = None
def _discover_volumes(
- session_dir: Path, embryo_id: Optional[str] = None
-) -> Dict[str, List[Tuple[datetime, Path]]]:
+ session_dir: Path, embryo_id: str | None = None
+) -> dict[str, list[tuple[datetime, Path]]]:
"""Discover volume files (with parsed acquisition timestamps) in a session directory."""
if not session_dir.exists():
return {}
tif_files = (
- list(session_dir.glob("*.tif")) + list(session_dir.glob("*.tiff"))
- + list(session_dir.glob("**/*.tif")) + list(session_dir.glob("**/*.tiff"))
+ list(session_dir.glob("*.tif"))
+ + list(session_dir.glob("*.tiff"))
+ + list(session_dir.glob("**/*.tif"))
+ + list(session_dir.glob("**/*.tiff"))
)
# Deduplicate (flat + recursive may overlap)
tif_files = list({f.resolve(): f for f in tif_files}.values())
- embryo_volumes = {}
+ embryo_volumes: dict = {}
for f in tif_files:
parts = f.stem.split("_")
@@ -89,6 +94,7 @@ def _discover_volumes(
def _load_volume(path: Path) -> np.ndarray:
"""Load a volume from TIFF file."""
from gently.core.imaging import load_volume
+
return load_volume(path)
@@ -122,9 +128,9 @@ def _create_three_view_image(volume: np.ndarray, max_dim: int = 1500) -> str:
_ensure_dependencies()
from gently.core.imaging import (
- projection_three_view,
- compute_crop_bounds,
apply_crop_bounds,
+ compute_crop_bounds,
+ projection_three_view,
)
# Auto-crop to embryo region
@@ -150,7 +156,7 @@ def _create_three_view_image(volume: np.ndarray, max_dim: int = 1500) -> str:
return base64.b64encode(buffer.getvalue()).decode("utf-8")
-def _create_separate_view_images(volume: np.ndarray, max_dim: int = 1000) -> Tuple[str, str]:
+def _create_separate_view_images(volume: np.ndarray, max_dim: int = 1000) -> tuple[str, str]:
"""Create separate TOP and SIDE view images from volume, return base64 tuple.
Parameters
@@ -244,7 +250,7 @@ def __init__(
self._embryo_volumes = _discover_volumes(self.session_path)
@property
- def embryo_ids(self) -> List[str]:
+ def embryo_ids(self) -> list[str]:
"""Get list of embryo IDs with both volumes and ground truth."""
gt_embryos = set(self.ground_truth.embryo_ids)
vol_embryos = set(self._embryo_volumes.keys())
@@ -258,7 +264,7 @@ def iter_embryo(
self,
embryo_id: str,
start_timepoint: int = 0,
- end_timepoint: Optional[int] = None,
+ end_timepoint: int | None = None,
) -> Iterator[TestCase]:
"""
Iterate through timepoints for an embryo sequentially.
@@ -318,7 +324,7 @@ def iter_embryo(
acquired_at=acquired_at,
)
- def iter_all(self) -> Iterator[Tuple[str, Iterator[TestCase]]]:
+ def iter_all(self) -> Iterator[tuple[str, Iterator[TestCase]]]:
"""
Iterate through all embryos in the testset.
diff --git a/benchmarks/perception/trace_viewer.py b/benchmarks/perception/trace_viewer.py
index 5a3ced2d..69ea5fa6 100644
--- a/benchmarks/perception/trace_viewer.py
+++ b/benchmarks/perception/trace_viewer.py
@@ -7,12 +7,9 @@
import argparse
import json
import sys
-from datetime import datetime
from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-from .metrics import format_metrics_summary, PerceptionMetrics
+from .metrics import PerceptionMetrics, format_metrics_summary
HTML_TEMPLATE = """
@@ -293,16 +290,16 @@
"""
-def generate_embryo_section(embryo_id: str, predictions: List[Dict]) -> str:
+def generate_embryo_section(embryo_id: str, predictions: list[dict]) -> str:
"""Generate HTML for one embryo's predictions."""
rows = [
'
",
]
for i, pred in enumerate(predictions):
@@ -320,15 +317,15 @@ def generate_embryo_section(embryo_id: str, predictions: List[Dict]) -> str:
row_id = f"{embryo_id}-{timepoint}"
rows.append(f'T${pred.timepoint}
- ${pred.predicted}
- ${pred.ground_truth}
+ ${pred.predicted}
+ ${pred.ground_truth}
${(pred.confidence * 100).toFixed(0)}%
- ${pred.phase_count > 1 ? pred.phase_count + '-phase' : ''}${verifiedBadge}
+ ${pred.phase_count > 1
+ ? pred.phase_count + '-phase' : ''}${verifiedBadge}
',
- '
',
+ "Timepoint
',
- 'Predicted
',
- 'Ground Truth
',
- 'Confidence
',
- 'Details
',
- 'Timepoint
",
+ "Predicted
",
+ "Ground Truth
",
+ "Confidence
",
+ "Details
",
+ "')
- rows.append(f'
""")
+ rows.append("")
# Reasoning trace (hidden by default)
trace_html = format_reasoning_trace(pred.get("reasoning_trace"))
- rows.append(f'''T{timepoint}
')
+ rows.append(f"T{timepoint}
")
rows.append(f'{pred_stage}
')
rows.append(f'{gt_stage}
')
- rows.append(f'''
+ rows.append(f"""
""")
# Details column with expand button
tool_calls = pred.get("tool_calls", 0)
@@ -351,31 +348,31 @@ def generate_embryo_section(embryo_id: str, predictions: List[Dict]) -> str:
if phase_count > 1:
badges += f'{phase_count}-phase'
- rows.append(f'''
{confidence:.0%}
-
''')
+
+ rows.append(f"""
')
+
{details_str}
{badges}
-
''')
- rows.append('
+ rows.append(f"""
""")
- return f'''
+ return f"""
Reasoning: {reasoning}
{trace_html}
-
''')
+ {embryo_id}
{"".join(rows)}
Tool Call: {tool_name}
Input: {json.dumps(tool_input, indent=2)}
- ''')
+ """)
elif step_type == "tool_result":
summary = step.get("tool_result_summary", content)
- html_parts.append(f'''
+ html_parts.append(f"""
Input: {json.dumps(tool_input, indent=2)}
Tool Result: {summary}
- ''')
+ """)
elif step_type == "final_decision":
- html_parts.append(f'''
+ html_parts.append(f"""
Final Decision:
{content[:500]}...
- ''')
+ """)
elif step_type == "verification_requested":
- html_parts.append(f'''
+ html_parts.append(f"""
{content[:500]}...
Verification Requested:
{content}
- ''')
+ """)
elif step_type == "verification_subagent":
tool_input = step.get("tool_input", {})
summary = step.get("tool_result_summary", content)
- html_parts.append(f'''
+ html_parts.append(f"""
{content}
- Subagent: {tool_input.get("stage_a", "?")} vs {tool_input.get("stage_b", "?")}
+ Subagent: {tool_input.get("stage_a", "?")} vs + {tool_input.get("stage_b", "?")}
Result: {summary}
- ''')
+ """)
elif step_type == "verification_result":
- html_parts.append(f'''
+ html_parts.append(f"""
+ Subagent: {tool_input.get("stage_a", "?")} vs + {tool_input.get("stage_b", "?")}
Result: {summary}
Verification Result:
{content}
- ''')
+ """)
return "".join(html_parts)
-def generate_confusion_matrix_html(confusion: Dict[str, Dict[str, int]]) -> str:
+def generate_confusion_matrix_html(confusion: dict[str, dict[str, int]]) -> str:
"""Generate HTML table for confusion matrix."""
- stages = ["early", "bean", "comma", "1.5fold", "2fold", "pretzel", "hatching", "hatched"]
+ stages = [
+ "early",
+ "bean",
+ "comma",
+ "1.5fold",
+ "2fold",
+ "pretzel",
+ "hatching",
+ "hatched",
+ ]
# Filter to stages present in data
present = set()
@@ -478,7 +485,7 @@ def generate_confusion_matrix_html(confusion: Dict[str, Dict[str, int]]) -> str:
return "".join(rows)
-def generate_html_report(report_data: Dict) -> str:
+def generate_html_report(report_data: dict) -> str:
"""Generate complete HTML report from benchmark data."""
# Extract summary metrics
metrics = report_data.get("metrics", {})
@@ -493,9 +500,7 @@ def generate_html_report(report_data: Dict) -> str:
# Generate embryo sections
embryo_sections = []
for er in embryo_results:
- embryo_sections.append(
- generate_embryo_section(er["embryo_id"], er["predictions"])
- )
+ embryo_sections.append(generate_embryo_section(er["embryo_id"], er["predictions"]))
# Generate confusion matrix
confusion = metrics.get("confusion_matrix", {})
@@ -569,8 +574,7 @@ def main():
# Filter embryo if specified
if args.embryo:
report_data["embryo_results"] = [
- er for er in report_data.get("embryo_results", [])
- if er["embryo_id"] == args.embryo
+ er for er in report_data.get("embryo_results", []) if er["embryo_id"] == args.embryo
]
# Generate HTML
diff --git a/benchmarks/runner.py b/benchmarks/runner.py
index 4d6d847a..7dd31236 100644
--- a/benchmarks/runner.py
+++ b/benchmarks/runner.py
@@ -13,8 +13,6 @@
import json
import logging
import sys
-from datetime import datetime
-from pathlib import Path
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger(__name__)
@@ -66,7 +64,6 @@ async def run_agent_benchmark(args):
def compare_reports(args):
"""Compare two benchmark reports"""
- from .agent.evaluator import BenchmarkReport, compare_reports as _compare
with open(args.before) as f:
before_data = json.load(f)
@@ -101,7 +98,10 @@ def compare_reports(args):
delta_str = f"+{fmt.format(delta)}" if delta > 0 else fmt.format(delta)
status = "improved" if delta > 0 else ("regressed" if delta < 0 else "unchanged")
- logger.info(f" {name}: {fmt.format(before_val)} -> {fmt.format(after_val)} ({delta_str}) [{status}]")
+ logger.info(
+ f" {name}: {fmt.format(before_val)} -> {fmt.format(after_val)}"
+ f" ({delta_str}) [{status}]"
+ )
# Token comparison
before_tokens = before_data.get("tokens", {})
@@ -111,7 +111,7 @@ def compare_reports(args):
after_total = after_tokens.get("total_input", 0) + after_tokens.get("total_output", 0)
token_delta = after_total - before_total
- logger.info(f"\nTokens:")
+ logger.info("\nTokens:")
logger.info(f" Total: {before_total:,} -> {after_total:,} ({token_delta:+,})")
return 0
diff --git a/config/config.yml b/config/config.yml
index e59e9f6d..9c05c274 100644
--- a/config/config.yml
+++ b/config/config.yml
@@ -1,4 +1,38 @@
organism: "celegans"
hardware: "dispim"
mmconfig: "MMConfig_tracking_screening.cfg"
-mmdirectory: "C:/Program Files/Micro-Manager-1.4"
\ No newline at end of file
+mmdirectory: "C:/Program Files/Micro-Manager-1.4"
+
+# SwitchBot Bot — physical button-pusher mounted on the diSPIM room light
+# switch. Talks BLE direct (no SwitchBot Hub / cloud). Plans address it by
+# name, e.g. `bps.mv(room_light, 'on')`. Remove this block to skip
+# registration; the device layer is tolerant of either state.
+switchbot:
+ name: room_light
+ address: "EC:6F:04:06:5B:23"
+ timeout: 20.0
+
+# ACUITYnano Precision Thermal Controller (Peltier/TEC, 0.0–99.9 °C). When this
+# block is present the device layer registers a `temperature` device and the
+# Devices tab shows a setpoint control; plans can also block on it via
+# `bps.mv(temperature, 20.0)`. Remove/comment the block to skip registration.
+#
+# backend: mqtt talks to the controller over the vendor's MQTT bridge
+# (acuitynano_precision_thermalizer_api). With no broker/port/user/password
+# keys it uses the vendor package's embedded HiveMQ Cloud defaults — set them
+# here only to point at a different broker. The vendor package must be
+# installed on the device-layer machine (not on PyPI).
+temperature:
+ name: temperature
+ backend: serial # serial | mqtt | mock — USB serial is the working
+ # link on this machine (MQTT cloud is firewalled)
+ com_port: "COM8"
+ baud_rate: 115200
+ stabilize_timeout: 600 # seconds to wait for "[ SYSTEM LOCKED ]" on a blocking set
+ feedback_peltier: false # true = control off the peltier sensor instead of water
+ # MQTT alternative (vendor HiveMQ Cloud defaults; needs outbound TLS :8883):
+ # backend: mqtt
+ # broker: "your-broker.example.com" # optional — overrides the embedded default
+ # port: 8883
+ # user: "username"
+ # password: "secret"
\ No newline at end of file
diff --git a/desktop/.gitignore b/desktop/.gitignore
new file mode 100644
index 00000000..6b3daa59
--- /dev/null
+++ b/desktop/.gitignore
@@ -0,0 +1,14 @@
+# Node / Tauri CLI
+node_modules/
+
+# Rust / Tauri build output
+src-tauri/target/
+src-tauri/gen/
+
+# Generated mobile / MS-Store icon variants (regenerate with `tauri icon`).
+# The desktop icons referenced by tauri.conf.json (32x32, 128x128, 128x128@2x,
+# icon.ico, icon.icns, icon.png) ARE committed so a clean checkout builds.
+src-tauri/icons/android/
+src-tauri/icons/ios/
+src-tauri/icons/Square*Logo.png
+src-tauri/icons/StoreLogo.png
diff --git a/desktop/README.md b/desktop/README.md
new file mode 100644
index 00000000..52e8b0ae
--- /dev/null
+++ b/desktop/README.md
@@ -0,0 +1,146 @@
+# Gently Desktop (Tauri shell)
+
+A thin [Tauri](https://v2.tauri.app) desktop wrapper that turns gently into a
+double-click Windows app. It **owns** the Python backend: on launch it spawns
+`launch_gently.py --no-browser`, shows a splash while the server boots, then
+renders the existing web UI in a native WebView2 window. No application logic
+lives here — the web UI served by Python stays the single source of truth.
+
+This is the desktop-packaging fold-in described in RFC #78
+(`docs/superpowers/specs/2026-07-02-unified-launcher-design.md`).
+
+## Why Tauri (and what it does / doesn't solve)
+
+- **Tiny footprint** — uses the OS WebView2 (already present on Win 11), not a
+ bundled Chromium.
+- **Robust process ownership** — the shell puts the spawned Python into a
+ Windows **Job Object** with `KILL_ON_JOB_CLOSE`. Python's own device-layer
+ grandchild (via `DeviceLayerSupervisor`) inherits the job, so when the app
+ quits *or crashes* the OS reaps the **entire** process tree — no orphaned
+ device layer holding COM ports.
+- **Not solved here:** bundling the Python environment (torch, anthropic,
+ perception) into a redistributable installer. This build launches the repo's
+ existing `.venv`. Shipping a self-contained `.msi`/`.exe` to another machine
+ needs an embeddable-Python / PyInstaller sidecar — see *Bundling* below.
+
+## Architecture
+
+```
+Tauri shell (Rust, WebView2 window)
+ └─ spawns: python launch_gently.py --no-browser [in a kill-on-close Job]
+ └─ spawns: start_device_layer.py (DeviceLayerSupervisor)
+ ├─ shows splash/index.html while uvicorn boots
+ └─ navigate() → http://localhost:8080 (the live gently UI)
+```
+
+- `src-tauri/src/main.rs` — spawn + Job Object teardown + wait-for-server + navigate.
+- `splash/index.html` — boot splash (Rust updates its status line via `window.__gentlyStatus`).
+- `src-tauri/tauri.conf.json` — one window, `frontendDist: ../splash`, NSIS bundle.
+
+## Prerequisites
+
+- **Rust** (MSVC toolchain) — `rustup default stable-x86_64-pc-windows-msvc`
+- **MSVC C++ build tools** (Visual Studio 2022 / Build Tools)
+- **WebView2 runtime** (inbox on Windows 11)
+- **Node** (only for the Tauri CLI: `npm install` in this folder)
+- A working gently checkout with its Python **`.venv`** at the repo root
+
+### Linux (dev machines)
+
+The shell is cross-platform — the Job-Object code is `#[cfg(windows)]`, so on
+Linux teardown is the best-effort `child.kill()` only (fine for dev; the
+kill-on-close guarantee is a Windows-production feature). Verified on Manjaro
+(KDE/Wayland, webkit2gtk 2.52):
+
+- **Rust** — `rustup default stable`
+- **WebKitGTK + GTK3** — `webkit2gtk-4.1` and `gtk3` dev packages
+ (Arch/Manjaro: `pacman -S webkit2gtk-4.1`; Debian/Ubuntu:
+ `apt install libwebkit2gtk-4.1-dev build-essential`)
+- **Node** — as above
+- The venv is found at **`.venv/bin/python`** (Unix layout) automatically;
+ `GENTLY_PYTHON` overrides as usual
+
+Same commands, bash syntax:
+
+```bash
+cd desktop && npm install
+GENTLY_LAUNCH_ARGS="--no-api --offline --no-auth" npm run dev
+```
+
+`npm run build` is Windows-only as configured (`bundle.targets: ["nsis"]`) —
+on Linux use `npm run dev`, or override targets if a Linux bundle is ever needed.
+
+## Run (dev)
+
+```powershell
+cd desktop
+npm install # once — restores the Tauri CLI
+npm run dev # build + launch the shell (spawns the backend for you)
+```
+
+`npm run dev` (= `tauri dev`) compiles the shell, opens the window, spawns
+`launch_gently.py --no-browser`, and navigates to the live UI once it's up.
+
+UI-only (no hardware / API key / login) — handy for pure UI work:
+
+```powershell
+$env:GENTLY_LAUNCH_ARGS="--no-api --offline --no-auth"; npm run dev
+```
+
+## Making code changes — what reflects, and how
+
+The window is just a WebView pointed at the Python server, so it splits in three:
+
+| You edit… | Reflects by… | Rebuild? |
+|---|---|---|
+| **Web UI** (`gently/ui/web/templates`, `static/js`, `static/css`) | **Refresh the window** (Ctrl+R) | no — served live by Python |
+| **Python backend** (`gently/**/*.py`, `launch_gently.py`) | restart the backend — or run with **`--reload`** (below), then Ctrl+R | process restart |
+| **Rust shell / config** (`src-tauri/`, `splash/`) | `npm run dev` **auto-rebuilds + relaunches** | automatic (watched) |
+
+**Python hot-reload.** Pass `--reload` and the backend auto-restarts whenever a
+`gently/*.py` file changes (watchfiles) — then just Ctrl+R the window:
+
+```powershell
+$env:GENTLY_LAUNCH_ARGS="--reload --no-api --offline --no-auth"; npm run dev
+# or, iterating in a plain browser instead of the shell:
+uv run python launch_gently.py --reload
+```
+
+It restarts the *whole* backend (a few seconds), so use it for UI / backend dev,
+not live hardware sessions.
+
+## Build (installer)
+
+```powershell
+cd desktop
+npm run build # = tauri build — NSIS installer under src-tauri/target/release/bundle/
+```
+
+Then Gently installs like any Windows app (Start-Menu entry, double-click — no
+terminal). The built app launches the **repo's** `.venv` + `launch_gently.py`
+(paths baked at compile time / overridable — see env vars); it's a single-machine
+build until Python bundling lands.
+
+## Configuration (env vars read by the shell)
+
+| Var | Default | Purpose |
+|---|---|---|
+| `GENTLY_HOME` | compile-time repo root | Directory to run the backend from |
+| `GENTLY_PYTHON` | `{content}
Gently
+
+ Starting the backend…
+ microscopy agent · desktop
+
+
+
diff --git a/desktop/src-tauri/Cargo.lock b/desktop/src-tauri/Cargo.lock
new file mode 100644
index 00000000..27a8fbb4
--- /dev/null
+++ b/desktop/src-tauri/Cargo.lock
@@ -0,0 +1,4433 @@
+# This file is automatically @generated by Cargo.
+# It is not intended for manual editing.
+version = 4
+
+[[package]]
+name = "adler2"
+version = "2.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "320119579fcad9c21884f5c4861d16174d0e06250625266f50fe6898340abefa"
+
+[[package]]
+name = "aho-corasick"
+version = "1.1.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ddd31a130427c27518df266943a5308ed92d4b226cc639f5a8f1002816174301"
+dependencies = [
+ "memchr",
+]
+
+[[package]]
+name = "alloc-no-stdlib"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cc7bb162ec39d46ab1ca8c77bf72e890535becd1751bb45f64c597edb4c8c6b3"
+
+[[package]]
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+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0e76a019e91224d279006ff972f1e984179a6e9feb050adba6ce8274aef23195"
+dependencies = [
+ "alloc-no-stdlib",
+]
+
+[[package]]
+name = "android_system_properties"
+version = "0.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "819e7219dbd41043ac279b19830f2efc897156490d7fd6ea916720117ee66311"
+dependencies = [
+ "libc",
+]
+
+[[package]]
+name = "anyhow"
+version = "1.0.103"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2a4385e2e34eb35d6b3efe798b9eb88096925d87726c0798709bf56d9ed84af3"
+
+[[package]]
+name = "atk"
+version = "0.18.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
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+dependencies = [
+ "atk-sys",
+ "glib",
+ "libc",
+]
+
+[[package]]
+name = "atk-sys"
+version = "0.18.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c5e48b684b0ca77d2bbadeef17424c2ea3c897d44d566a1617e7e8f30614d086"
+dependencies = [
+ "glib-sys",
+ "gobject-sys",
+ "libc",
+ "system-deps",
+]
+
+[[package]]
+name = "atomic-waker"
+version = "1.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1505bd5d3d116872e7271a6d4e16d81d0c8570876c8de68093a09ac269d8aac0"
+
+[[package]]
+name = "autocfg"
+version = "1.5.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f2032f911046de80f0a198e0901378627c33f59ea0ac00e363d481118bd70a53"
+
+[[package]]
+name = "base64"
+version = "0.21.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567"
+
+[[package]]
+name = "base64"
+version = "0.22.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
+
+[[package]]
+name = "bit-set"
+version = "0.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "08807e080ed7f9d5433fa9b275196cfc35414f66a0c79d864dc51a0d825231a3"
+dependencies = [
+ "bit-vec",
+]
+
+[[package]]
+name = "bit-vec"
+version = "0.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5e764a1d40d510daf35e07be9eb06e75770908c27d411ee6c92109c9840eaaf7"
+
+[[package]]
+name = "bitflags"
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+version = "0.1.0"
+edition = "2021"
+description = "Gently desktop shell — owns the Python backend + device layer"
+authors = ["gently"]
+default-run = "gently-desktop"
+
+[build-dependencies]
+tauri-build = { version = "2", features = [] }
+
+[dependencies]
+tauri = { version = "2" }
+
+# Windows Job Object: put the spawned Python (and its device-layer grandchild)
+# in a kill-on-close job so the whole tree dies when the shell exits or crashes.
+[target.'cfg(windows)'.dependencies]
+windows = { version = "0.62", features = [
+ "Win32_Foundation",
+ "Win32_System_JobObjects",
+ "Win32_System_Threading",
+ "Win32_Security",
+] }
+
+[profile.release]
+codegen-units = 1
+lto = true
+opt-level = "s"
+strip = true
diff --git a/desktop/src-tauri/build.rs b/desktop/src-tauri/build.rs
new file mode 100644
index 00000000..d860e1e6
--- /dev/null
+++ b/desktop/src-tauri/build.rs
@@ -0,0 +1,3 @@
+fn main() {
+ tauri_build::build()
+}
diff --git a/desktop/src-tauri/capabilities/default.json b/desktop/src-tauri/capabilities/default.json
new file mode 100644
index 00000000..c1da0cbb
--- /dev/null
+++ b/desktop/src-tauri/capabilities/default.json
@@ -0,0 +1,7 @@
+{
+ "$schema": "../gen/schemas/desktop-schema.json",
+ "identifier": "default",
+ "description": "Thin shell — the UI is served by the Python backend and makes no Tauri IPC calls. Navigation is driven from Rust, so no window/shell permissions are needed.",
+ "windows": ["main"],
+ "permissions": ["core:default"]
+}
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diff --git a/desktop/src-tauri/src/main.rs b/desktop/src-tauri/src/main.rs
new file mode 100644
index 00000000..ff706c69
--- /dev/null
+++ b/desktop/src-tauri/src/main.rs
@@ -0,0 +1,247 @@
+// Gently desktop shell.
+//
+// A thin Tauri (WebView2) window that OWNS the Python backend. On launch it
+// spawns `launch_gently.py --no-browser`, shows a splash while uvicorn boots,
+// then navigates the window to the live UI (http://localhost:
+
+
+"""
+
+
+def main():
+ ap = argparse.ArgumentParser(description="Temporary SwitchBot room-light web GUI")
+ ap.add_argument("--address", default=DEFAULT_ADDRESS, help="Bot BLE MAC address")
+ ap.add_argument("--port", type=int, default=8765)
+ ap.add_argument("--host", default="127.0.0.1", help="bind host (default: localhost only)")
+ args = ap.parse_args()
+
+ logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+ global BOT
+ BOT = Bot(args.address)
+ print(f"\n diSPIM Room Light GUI → http://{args.host}:{args.port}\n Bot: {args.address}\n")
+ uvicorn.run(app, host=args.host, port=args.port, log_level="warning")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
new file mode 100644
index 00000000..cd2621f9
--- /dev/null
+++ b/docs/ARCHITECTURE.md
@@ -0,0 +1,171 @@
+# Gently — Concurrency & Runtime Architecture
+
+How the system runs temperature telemetry, device-state polling, experiments,
+image acquisition, and perception/VLM "at the same time" without stepping on
+itself. The short version: **almost nothing runs truly in parallel — the design
+quarantines the blocking work and serializes the hardware, then keeps both event
+loops responsive by offloading and decoupling everything slow.**
+
+> Scope: the concurrency model. For storage layout see `CLAUDE.md`; for the
+> device/hardware plugin model see `docs/asi-plugin-architecture.md`.
+
+## 1. Two processes, bridged by HTTP + a shared filesystem
+
+This split is the load-bearing fact of the whole design.
+
+```
+┌────────────────────────────────────────┐ ┌────────────────────────────────────────┐
+│ APP / VIZ process (FastAPI :8080) │ │ DEVICE-LAYER process (aiohttp :60610) │
+│ gently/app/agent.py │ │ gently/hardware/dispim/device_layer.py │
+│ │ HTTP │ │
+│ • agent + TimelapseOrchestrator │◄──────►│ • the ONLY code touching hardware: │
+│ • Perceiver (gently_perception, VLM) │ (DiSPIM│ MMCore/pymmcore, Ophyd, Bluesky RE │
+│ • AsyncAnthropic clients │ Client)│ • plan queue + single executor │
+│ • EventBus + WebSocket ConnectionMgr │ │ • 3 state pollers + camera/LS streamers │
+│ • TemperatureSampler, DeviceStateMonitor│ │ │
+└──────────────┬──────────────────────────┘ └───────────────┬──────────────────────────┘
+ │ shared filesystem (incoming/ TIFF staging, session store)
+ └──────────────────────────────────────────────────┘
+```
+
+- The **device-layer process** (`device_layer.py`) is the *only* code that ever
+ touches the microscope: MMCore/pymmcore, the Ophyd devices, and a Bluesky
+ `RunEngine`. All truly blocking hardware work is quarantined here.
+- The **app/viz process** (`app/agent.py`, FastAPI+uvicorn) owns everything
+ cognitive and user-facing: the agent, the `TimelapseOrchestrator`, the
+ `Perceiver` (external `gently_perception` VLM package), the Anthropic clients,
+ the in-process `EventBus`, the WebSocket `ConnectionManager`, and the pollers.
+- The app reaches hardware **only** through `DiSPIMClient` (`client.py`), over a
+ single shared `aiohttp.ClientSession`. It never imports MMCore.
+
+Because blocking hardware calls live in a separate process, the app-side loop
+stays a light cooperative-asyncio world.
+
+## 2. One asyncio event loop per process
+
+- **Device loop:** HTTP routes + the RunEngine driver + the plan queue/executor +
+ three state pollers (XY ~5 Hz, piezo/galvo ~1 Hz, full property cache ~15 s) +
+ subscriber-gated camera/lightsheet SSE streamers.
+- **App loop:** FastAPI + the `TimelapseOrchestrator` acquisition loop +
+ `TemperatureSampler` (1 Hz) + `DeviceStateMonitor` + `Perceiver` calls + one
+ coroutine per open browser WebSocket.
+
+## 3. The core trick: hardware is serialized, not parallelized
+
+Every Bluesky plan — a move, a snap, a volume/burst acquisition, a focus/calibration
+sweep — is submitted as a `PlanRequest` onto **one `asyncio.Queue`
+(`self._plan_queue`), drained by one `_plan_executor` task**. Only one plan owns
+the hardware at a time.
+
+- `submit_plan` enqueues and `await`s a per-request `asyncio.Future`; the executor
+ sets the result/exception, which flows back to the awaiting HTTP handler. On
+ failure the executor records it in `_plan_execution_log` and continues to the
+ next queued plan.
+- Underneath, **pymmcore's internal `g_core_lock` is the real mutex** serializing
+ every actual core call across pollers *and* plans. The `DiSPIMSystem` facade is
+ the single place the process touches MMCore.
+
+A microscope has one stage and one camera; single-file execution is correct, not
+a limitation.
+
+## 4. How constant polling coexists with long experiments
+
+Four cooperating mechanisms keep the loops responsive while a plan runs:
+
+1. **Offload blocking reads.** Every MMCore read, camera grab, SAM call, and
+ transient temperature probe goes through `asyncio.to_thread`; the camera
+ sequence runs on its own `threading.Thread` returning an Ophyd `Status`. The
+ loop itself never sits on I/O.
+2. **Split pollers by cadence.** The three device-state pollers are independent
+ tasks so a slow (~1.5 s) full-state-cache read cannot stall the ~5 Hz XY path.
+3. **`pause_state_updates()` — a reference-counted async context manager.** Heavy
+ plans (a `frozenset` of names) wrap execution in it, incrementing a counter;
+ every poller/streamer checks `if self._state_pause_counter > 0` and *skips its
+ MMCore read*, emitting only ~2 s heartbeats. The plan gets full serial/camera
+ bandwidth instead of the pollers fighting it for `g_core_lock`. Nested heavy
+ sections stack and unwind cleanly.
+4. **Telemetry bypasses the plan queue.** `GET /api/temperature/status` reads the
+ temperature Ophyd device directly (`temp.read()` over serial/MQTT — a device
+ wholly separate from MMCore); `GET /api/devices/state` returns the cached
+ `_state_latest` snapshot. Neither sits behind a running experiment, so status
+ polls are never blocked by a long acquisition. (MMCore push callbacks also
+ mirror joystick/property changes into `_state_latest` via
+ `loop.call_soon_threadsafe` with ~50 ms debouncing.)
+
+### Temperature specifically
+The vendor SDK backend (serial or MQTT) runs its own **background daemon thread**
+that ingests the controller's 500 ms telemetry broadcast into a cache
+(`self.telemetry`); `get_water_temp()`/`get_system_state()` return the cached
+value (non-blocking). `TemperatureController.read()` returns that cache, and
+`TemperatureSampler` (`interval_sec=1.0`) polls it at 1 Hz → persists + emits
+`TEMPERATURE_UPDATE`. So we ride the telemetry indirectly, resampled at 1 Hz.
+
+## 5. Image data travels via the filesystem, not JSON
+
+Arrays over ~1 MB are written as **TIFF into the shared `incoming/` staging dir**;
+only a small `{__file_ref__, path, shape, dtype}` dict crosses HTTP (`serialize_value`).
+The client resolves the ref with `tifffile` and hands the decoded array to
+`register_volume`, which renames the file into the session store and stamps it
+with the latest temperature sample — avoiding a multi-GB JSON blob and a double
+decode.
+
+## 6. Perception / VLM / events are decoupled (fire-and-forget)
+
+- **Perception never gates acquisition.** The `TimelapseOrchestrator` does
+ `asyncio.create_task(self._run_perception(...))` rather than awaiting it inline,
+ so a slow VLM call doesn't hold up the next embryo. Inside the task the
+ `Perceiver` and the Claude client are awaited cooperatively, and every Claude
+ call is wrapped in `asyncio.wait_for(timeout=30)` returning a safe fallback
+ instead of raising into the loop.
+- **The `EventBus` publishes without awaiting handlers** (`event_bus.py`): sync
+ handlers run inline, async handlers are scheduled via `asyncio.ensure_future` /
+ `loop.call_soon_threadsafe`; high-volume telemetry types skip the bounded
+ history deque, so one slow WebSocket client delays only its own broadcast.
+
+## 7. Backpressure & failure isolation
+
+- **SSE streams** use per-subscriber queues bounded at `maxsize=4`; device-state
+ broadcasts drop slow subscribers, camera/lightsheet streams drop the *oldest*
+ frame and push the newest so steady clients keep fresh frames.
+- **WebSocket fan-out** (`ConnectionManager.broadcast`) sends per client under an
+ `asyncio.Lock` and drops clients that error.
+- **Failure = a gap, not a crash.** A failed temperature poll logs once and backs
+ off (`1.0 s → min(interval*10, 30 s)`); a stalled SSE forces a watchdog
+ reconnect after 60 s (chosen to tolerate the quiet windows during heavy plans);
+ volume acquisition always disables lasers on error to protect the sample.
+- **Config safety.** `POST /api/temperature/config` refuses (`409`) while the
+ RunEngine is not idle or a ramp holds the controller lock; `health_check()` is
+ read-only and never flips the connected flag, so a transient status-poll timeout
+ can't disconnect an in-flight acquisition.
+
+## 8. Known limits & bottlenecks
+
+- **RunEngine on the loop.** `self.RE(plan)` is invoked synchronously on the
+ device-layer loop, so while a plan runs the loop is largely occupied — which is
+ exactly why heavy plans quiet the pollers and the app-side watchdog tolerates
+ 60 s of silence. Whether the telemetry HTTP handlers stay fully responsive
+ mid-acquisition depends on Bluesky's internal threading (external package) and
+ is not verified from repo code.
+- **Unbounded in-flight perception.** `_perception_tasks` is a plain set that
+ self-prunes; sustained fast acquisition against a slow VLM could grow concurrent
+ Claude calls without an explicit cap. A semaphore is the obvious guard if
+ cadence is ever pushed.
+- **~~Synchronous O(n) prediction writes~~ — FIXED.** `store_prediction` used to
+ re-parse the entire `predictions.jsonl` on every append to compute the next id.
+ Now O(1) via a bounded tail read (`_last_jsonl_record`, `file_store.py`). Other
+ `FileStore` JSONL writes remain synchronous on the app loop but are single-line
+ appends.
+- **External VLM internals.** How `gently_perception`'s `Perceiver` implements its
+ VLM call (async httpx vs sync-in-thread) is not inspectable from this repo; the
+ orchestrator awaits it, implying a coroutine.
+
+## The model in one line
+
+Two single-threaded event loops; hardware fenced into one process behind a
+one-at-a-time plan queue; everything blocking pushed into threads; and the
+slow/cognitive work (VLM, persistence, UI) decoupled with fire-and-forget tasks —
+so the system *feels* concurrent while the microscope itself stays strictly
+serialized.
+
+---
+*Generated from a code-grounded architecture pass (Claude Opus 4.8), 2026-07-01.*
diff --git a/docs/CLOSED_LOOP_PARADIGM.md b/docs/CLOSED_LOOP_PARADIGM.md
new file mode 100644
index 00000000..8f1cf80d
--- /dev/null
+++ b/docs/CLOSED_LOOP_PARADIGM.md
@@ -0,0 +1,536 @@
+# Closed-Loop Paradigm: Notes on the Discussion
+
+This document captures the design conversation that produced everything on the
+`paradigm/closed-loop` branch: the schema split, the Map-as-embryo-home work,
+the operator-action vocabulary, the eval substrate (capture / replay /
+decisions / shadow), and the trajectory the system is on. It is a
+distillation, not a transcript — a future-self / new-collaborator reference
+for *why* this code looks the way it does and *where it is going*.
+
+---
+
+## 1. The Original Friction
+
+The conversation started from a small, concrete observation by the operator:
+
+> "It feels awkward that the operator has to go between the chat in the TUI
+> and the viz server… or even to chat about detecting embryos."
+
+That awkwardness is a symptom, not a defect. It surfaces a deeper design
+question: **what is the orchestrator (the agent) actually for?** Today the
+orchestrator does at least four jobs at once, and one of them — *tool router*
+— is the one creating the friction.
+
+### The four orchestrator roles
+
+| Role | Description | Replaceable by a button? |
+| --- | --- | --- |
+| 1. Tool router | "Detect embryos" → `detect_embryos()` call | **Yes** — this is the friction surface |
+| 2. Workflow runner | Timelapses, multi-embryo plans, perception loops | No |
+| 3. Domain reasoner | Knows microscopy, embryos, safety constraints | No |
+| 4. Session memory | Coherent narrative of what happened and why | No |
+
+Routing a single click through chat for a routine action is the system
+fighting against its own users. Routing a multi-step scientific decision
+through Claude is using the right tool for the right job. The paradigm here
+is: **shrink role 1 to its essentials, keep roles 2–4 first-class, and let
+the UI carry the rest.**
+
+---
+
+## 2. Web ↔ Chat Reconciliation Patterns
+
+Four ways to relate the web UI and the chat orchestrator. Each has a
+distinct world model property:
+
+### A. Chat-only intent (the old default)
+
+Every action originates in chat. The web is observation + delegated subtasks
+(e.g. the marking canvas is a delegation the orchestrator triggers).
+
+* Cleanest record.
+* Worst friction.
+* Orchestrator's world model is "complete" because every change passes
+ through it.
+
+### B. Two parallel command surfaces
+
+Operator clicks in web, web acts directly; orchestrator finds out by polling
+state or doesn't find out at all.
+
+* Lowest friction.
+* Orchestrator's world model **drifts from reality** — fatal for role 4
+ (session memory) and dangerous for role 3 (safety reasoning).
+
+### C. Web acts, orchestrator subscribes *(the chosen direction)*
+
+Operator clicks → web performs the action **and** publishes an event
+(`OPERATOR_*`) → orchestrator's session memory ingests it.
+
+* Chat log shows only human conversation.
+* Orchestrator's working context shows chat + events as a single timeline.
+* Phase 7 (operator events vocabulary + reactive candidate) is the first
+ installment of this pattern.
+
+### D. Cross-pattern hybrid
+
+Different action classes use different patterns. Heavy / novel / composite
+actions use chat (A); routine / clickable / contextual actions use web (C).
+This is what the system actually drifts toward; pattern C is the substrate
+that makes it possible.
+
+The orchestrator's job shifts from being **a funnel for action** to being **a
+brain that knows what's happening on every surface**.
+
+---
+
+## 3. The "Turn" is Wrong; the "Decision Moment" is Right
+
+Chat-AI literature reasons in *turns* (user message → assistant response).
+That model imports an assumption that does not hold here: the human is at
+the keyboard continuously. In a microscopy experiment running 12+ hours, the
+human checks in once, twice, maybe ten times. The agent is autonomous in
+between.
+
+The right unit is a **decision moment**, triggered by:
+
+1. **User message** — rare, interrupting (classic chat turn).
+2. **Critical event** — error, safety violation, lost focus, perception
+ anomaly. Wake immediately; decide to act / abort / escalate.
+3. **Phase boundary** — between timepoints, between embryos. Built-in
+ checkpoint: review state, decide whether to continue.
+4. **Periodic checkpoint** — every N minutes if nothing else happened.
+ Catches slow drifts.
+
+Between moments the agent is asleep. Plans execute autonomously. Events
+accumulate on the bus and in the world model. When the next decision
+moment fires, the agent reads:
+
+* The trigger (why am I waking up?)
+* The world snapshot (NOW state)
+* The events digest (what happened since last wake)
+* The conversation history (which might be hours old and less relevant
+ than usual)
+
+This is closer to a **supervisory controller** than a chat partner. The
+conversation history matters less than usual; what matters more is the
+**flight log** (events) plus the **current state snapshot**.
+
+### Trigger model — concrete
+
+A small router (in code, not Claude) sits between the bus and the brain:
+
+```
+ user input ─┐
+ event bus ──┼─► wake-router ──► (compose context) ──► claude.messages.create
+ schedule ──┘
+```
+
+The router's responsibilities:
+* Subscribe to a whitelist of "wake-worthy" event types.
+* Hold a debounce / coalescing buffer (so a burst of events becomes one
+ wake).
+* Keep a heartbeat schedule (every N minutes if no other trigger fired).
+* On wake, package: trigger, world snapshot, events digest, recent
+ conversation tail.
+* Surface the package to the brain.
+
+The brain stays the brain (Claude). The router is cheap, deterministic,
+debuggable code. It's the **meta-orchestrator** the operator mentioned —
+**not as another LLM**, but as a control surface.
+
+### Phase boundaries: hand-back vs subscribe
+
+Two designs for letting the brain look in mid-plan:
+
+* **Plan hands control back** at well-known points (between embryos, every
+ 5 timepoints). Cheaper, predictable, slightly less reactive.
+* **Plan never pauses; brain subscribes to plan events** ("perception
+ complete for embryo 3"). More reactive, more plan-coupling.
+
+The first one composes better with the supervisory-controller framing and
+is the recommended starting point.
+
+### Idle ticks
+
+If 30 min pass with no event and no user, should the agent wake to verify
+everything's OK? Default to **yes — periodic ticks with a high action
+threshold.** Most ticks should result in the agent doing nothing. The
+purpose is catching slow drifts (focus, sample state, hardware
+degradation) that don't trigger their own events.
+
+---
+
+## 4. World Model — Tiered, Not Monolithic
+
+A common mistake is "summarise everything every turn." Better is a tiered
+model where different freshness/density tiers carry different cadence
+costs.
+
+### Tier 1 — World snapshot
+
+Structured, ~30 lines, computed from in-memory state (not events), every
+wake.
+
+Includes: live stage XY/Z, current session id, embryo list with
+calibration state, current plan, acquisition status, recent operator
+actions (one-line summary).
+
+Cheap to build, fresh every time. Already mostly present in the
+codebase — `agent.experiment.get_summary()` plus the cached
+`DEVICE_STATE_UPDATE` payload is 80% of this.
+
+### Tier 2 — Recent-events digest
+
+Hand-written formatter over the events bus, filtered to wake-worthy types,
+inserted as a system note at each wake.
+
+Shape: `"Since last response: operator added embryo 4 via Map at 14:32; calibration completed for embryo 2; one perception trace pending."`
+
+Hand-written because LLM summarisation here adds latency, cost, and
+non-determinism for low value. Events are already structured.
+
+### Tier 3 — Pull tools
+
+For when reasoning needs depth: `get_recent_perceptions(embryo_id, n=5)`,
+`get_session_timeline()`, `get_learnings(campaign_id)`, etc. The agent
+calls these when it wants the detail.
+
+### Tier 4 — Optional LLM summariser
+
+Reserved for genuinely natural-language streams that resist rule-based
+compression: accumulated CV reasoning chains, narrative observations,
+cross-session learnings. Use a smaller, faster Claude model (Haiku is the
+natural fit). Run lazily, when a tier-3 tool asks for "summarise the last
+30 min for embryo 3."
+
+### Why this shape
+
+Decision moments are **rare** in autonomous mode. Token budget per wake
+can be generous (it's mostly idle compute). What matters more than budget
+is **cadence of waking** — saving 200 tokens per turn doesn't help if
+you're waking up at the wrong moments.
+
+---
+
+## 5. Testing — Where Most Projects Fail
+
+You cannot iterate on agent architecture without a way to compare
+architectures. Microscopy makes this hard:
+
+* Physical, non-deterministic, non-replayable in the trivial sense.
+* "Correct" is fuzzy — biological judgements rarely have ground truth.
+* Slow feedback (a timelapse takes hours).
+* Can't always reset to a clean state (samples are consumed).
+
+Five testing primitives, ranked by payoff per unit work (this ordering
+informed Phase 6's build order):
+
+### 5.1 Event replay *(built — Phase 6a/6b)*
+
+Capture the full event stream during a real run. Offline, replay it
+through any candidate architecture. Diff its decisions against
+production's. **Foundation** — without it, every change to the
+orchestrator is a flight test.
+
+### 5.2 Shadow mode *(built — Phase 6d)*
+
+During a real experiment, candidate architectures run alongside
+production. They see the same events but their decisions are *logged,
+not enacted*. Unique value over pure replay: shadow agents experience
+real temporal cadence, so timing-sensitive things (drift, races) are
+caught.
+
+### 5.3 Synthetic event sequences
+
+Hand-crafted streams: cascading errors, ambiguous perception,
+contradictory operator actions, focus drift, network drop mid-acquisition.
+Stress / chaos testing. The orchestrator is correct if it doesn't do
+something catastrophic — much easier to score than biological
+correctness.
+
+Trivially built on top of 5.1 — write a `jsonl` by hand, replay it.
+
+### 5.4 Decision-level micro-benchmarks
+
+Specific judgements — "given this perception result and these recent
+observations, should the agent re-focus?" — captured as
+(input → expected decision) pairs labelled by a biologist. Regression
+suite. Cheap with biologist time, expensive to bootstrap, very valuable
+once you have a few hundred.
+
+### 5.5 Multi-agent A/B in production
+
+Two embryos in the same dish, one supervised by architecture A and one
+by B (both honouring the firmware fence). Compare biological outcomes.
+Slow (one timelapse per data point), but the **only thing that measures
+biological correctness end-to-end.**
+
+---
+
+## 6. Embryo Schema: Coarse vs Fine
+
+Foundational and quietly important. Each embryo carries:
+
+* `position_coarse` — set by bottom-camera detection or manual Map
+ placement. Always present.
+* `position_fine` — set later by SPIM-objective alignment (workflow not
+ yet built). Initially `{}`.
+* `stage_position` — a *derived property*: `fine if fine else coarse`.
+ Downstream motion / perception keeps reading this and stays agnostic
+ about which calibration stage we're in.
+
+This is the seed for a broader idea: **measurements have provenance and
+calibration state**. The same embryo at the same nominal XY can have
+different "true" positions depending on which sensor sighted it. Encode
+that explicitly so any downstream decision can ask *"how confident is
+this position?"* without needing to know the whole calibration history.
+
+When the operator drags an embryo on the Map, the PUT clears `fine` —
+overriding the sighting invalidates any SPIM-derived fine alignment
+derived from the old coarse. `OPERATOR_EDITED_EMBRYO` carries
+`fine_position_invalidated` so the candidate / future controller can
+schedule a re-alignment without inferring it.
+
+---
+
+## 7. The Map as Collaborative World Model
+
+The Devices > Map page is more than visualisation. It is the **first
+collaborative surface** between operator and orchestrator: both can read
+the embryo list; both can mutate it. The orchestrator subscribes; the
+operator clicks.
+
+Visual semantics matter:
+
+* Coarse-only embryo → outlined ring + number. *Provisional.*
+* SPIM-fine-calibrated → filled disc + number. *Committed.*
+
+Calibration state is then visible at a glance across the slide — the
+operator can scan and see "embryo 3 still needs alignment" without
+opening anything.
+
+The pick-up / drop interaction (Phase 5) deliberately rejects
+click-to-add: the Map is a schematic, not a satellite view. Adding a
+new sighting without a visual reference is guessing. New embryos go
+through the bottom-camera marking canvas. The Map is for **editing what
+already exists**.
+
+### Future arc
+
+* **Annotations beyond position**: operator marks "this is the control",
+ "this one is dead, skip", "I think this is in 2-cell stage". These
+ become first-class scientific observations through additional
+ `OPERATOR_*` events.
+* **Satellite tile**: render the live bottom-camera frame as an overlay
+ on the Map at the current stage XY, scaled by um_per_pixel. Inside
+ that tile, click-to-add becomes meaningful (you can see what you're
+ picking). Outside, the Map stays schematic.
+
+---
+
+## 8. Revolutionary Trajectories
+
+Some of these are reasonable extensions; some are genuinely new.
+
+### 8.1 Plans-as-goals, not scripts
+
+Operator specifies "characterise gut development for these four
+embryos." Orchestrator translates this into a continuously adapted
+imaging plan that changes based on what perception sees mid-run. The
+plan isn't a fixed script handed to Bluesky — it's a negotiation the
+orchestrator keeps in flight, with the world model as the substrate
+for adaptation.
+
+Requires: tier-1 + tier-2 world model, decent perception loop, a way
+to express goals as predicates over the world model.
+
+### 8.2 Compounding cross-session learning
+
+`agent/learnings/` already exists. Today it's barely used. With replay
++ shadow, an architecture that proposes priors ("embryos at 3-fold
+typically need slower piezo") becomes **A/B testable across sessions**.
+Improvement gets *measurable*, which is the unlock — most "smart
+microscopy" today is shallow because it has no measurement loop.
+
+The right framing: each session is a **trial**, the orchestrator is the
+**experimenter**, the world model is what carries learning between
+trials.
+
+### 8.3 Collaborative world model
+
+The Map (operator edits embryos) is the first instance. Extend
+everywhere:
+
+* Operator annotates morphology on the Map → orchestrator updates
+ hypothesis space.
+* Operator marks a focus failure → orchestrator marks the calibration
+ region as untrustworthy.
+* Operator confirms a perception → orchestrator increases confidence in
+ the perception predicate for similar inputs.
+
+The point is making the operator's tacit knowledge **first-class data**
+that the system can reason about, not just record.
+
+### 8.4 Reverse-mode microscopy
+
+"I want to know X — plan the imaging that answers X." The orchestrator
+translates scientific goals into imaging plans. This is the
+plans-as-goals idea taken to its conclusion: the operator describes
+intent in scientific terms, the orchestrator owns the imaging strategy.
+
+Tractable only once 8.1 and the goal language are built.
+
+### 8.5 Continuous shadow / always-on critic
+
+Run the production orchestrator + a shadow candidate continuously, and
+log all decision divergences. Over weeks, the divergence log becomes a
+**dataset of disagreements**. Each disagreement is either:
+
+* Production was right, candidate was wrong → candidate needs a fix.
+* Candidate was right, production was wrong → consider promotion or
+ investigate why production picked differently.
+* Both were defensible → annotate the case.
+
+Free with the eval substrate (Phase 6); the only addition is a
+divergence collator.
+
+---
+
+## 9. Concretely Built Today (`paradigm/closed-loop` branch)
+
+| # | Commit | What |
+| --- | --- | --- |
+| 1 | `3e410581` | Schema split: `position_coarse` / `position_fine` / derived `stage_position`. |
+| 2 | `617e54c9` | `ExperimentState.notify_embryos_changed()` observer → `EMBRYOS_UPDATE` broadcast. |
+| 3 | `144d9fc9` | Map render layer — lavender rings (coarse) / discs (fine) / numbers. |
+| 4 | `4fbb9edf` | `detect_embryos` flows through web Marking canvas; `auth.py` + `require_control`. |
+| 5 | `8f6553e1` | Map pick-up / drop / Delete to edit embryos in place (control-gated PUT/DELETE). |
+| 6 | `808fe813` | Side-fix: re-enable XY joystick at device-layer boot. |
+| 7 | `f7a13d69` | Side-fix: image-anchored crosshair + scroll-to-zoom in camera panel. |
+| 8 | `d69cc219` | `gently/eval/`: event capture / replay / shadow / decision log scaffolding. |
+| 9 | `75d7c9db` | Production decision capture wired through `ConversationManager.call_claude`. |
+| 10 | `0a97563e` | `OPERATOR_*` event vocabulary + `ReactiveCandidate` (first real shadow). |
+
+### Per-session disk shape now
+
+`D:\Gently3\sessions\{id}\`
+
+* `events.jsonl` — captured event bus, telemetry-filtered.
+* `decisions.jsonl` — every Claude turn (success + error).
+* `interaction_log.jsonl` — pre-existing chat-shaped interactions.
+* `timeline.jsonl` — pre-existing session timeline.
+* Plus everything from the legacy FileStore layout.
+
+### Eval CLI
+
+`python scripts/replay_session.py {session_id_prefix} [--histogram] [--candidate {name}] [--real-time] [--time-scale N]`
+
+---
+
+## 10. What is *Not* Done Yet
+
+These are the natural follow-ups; sketched as future-self breadcrumbs.
+
+### Near-term (days)
+
+* **Tier-1 world snapshot** as a system-prompt section the brain sees
+ on every wake. Build the snapshot from `agent.experiment` plus the
+ last cached `DEVICE_STATE_UPDATE`. ~30 lines of formatted prose, every
+ wake.
+* **Tier-2 events digest** — hand-written formatter that reads the
+ bus's recent meaningful events (or the captured jsonl tail) and
+ produces a one-paragraph "since last response" note.
+* **Snapshot ingest into the brain's prompt** — `_update_system_prompt`
+ already takes a `context_summary`; route tier-1 + tier-2 through it.
+
+### Medium-term (weeks)
+
+* **Wake-router** — the code-level scheduler from §3. Currently the
+ brain only wakes on user message. Add: event-driven wake (subscribe
+ to wake-worthy events), periodic-tick wake (heartbeat), debounce /
+ coalesce buffer.
+* **More operator events** — `OPERATOR_ANNOTATED_EMBRYO` ("this is the
+ control", "skip, looks dead"), `OPERATOR_STARTED_TIMELAPSE`,
+ `OPERATOR_INTERRUPTED_PLAN`, `OPERATOR_TOGGLED_CAMERA`. Whatever the
+ Map / web UI lets the operator do should publish a typed event.
+* **SPIM-fine alignment workflow** — populate `position_fine`. Tool +
+ per-embryo state transition. Triggers `EMBRYOS_UPDATE` and a new
+ `FINE_ALIGNMENT_COMPLETED` event the orchestrator can react to.
+* **Continuous-shadow harness** — extend `ShadowRunner` to run a
+ candidate alongside production in the live agent process (not just
+ during replay). Collect divergences into a per-session
+ `divergences.jsonl`.
+
+### Longer arc (months)
+
+* **A goal expression language** — predicates over the world model that
+ let the operator say "image until 4-fold" or "follow the cell
+ divisions in embryo 3 at high resolution." This is the substrate for
+ §8.1 (plans-as-goals).
+* **LLM-driven candidates** — once the rule-based `ReactiveCandidate`
+ proves the substrate, add Claude-driven candidates (Haiku for cheap,
+ Opus for thinking). Use the snapshot+digest as their input.
+* **Cross-session learning loop** — wire the `learnings/` store into
+ the world model as priors. Add a learning-write surface (a tool the
+ orchestrator can call when it notices a pattern). Use shadow A/B to
+ validate that learnings improve decisions.
+* **Goal-driven evaluation** — once goals exist, "did the experiment
+ achieve its goal" becomes a measurable end-to-end success rate. The
+ ultimate metric is this, not turn-level decision diffs.
+
+---
+
+## 11. Principles That Surface Throughout
+
+A few recurring design priors worth naming:
+
+1. **Distill, don't dump.** Structured summaries beat raw logs in
+ prompts. Hand-written formatters beat LLM summarisers for
+ structured data. LLMs for prose, code for structure.
+2. **Pull beats push when uncertain.** Default to tools the agent
+ queries on demand, not data shoved into every prompt. Push only
+ what's universally relevant (the world snapshot).
+3. **Same shape for production and shadow.** If production writes a
+ Decision with these fields, shadow candidates write Decisions with
+ the same fields. Diff is then trivial.
+4. **Events carry intent; state carries position.** `EMBRYOS_UPDATE`
+ is state (the embryo list now). `OPERATOR_EDITED_EMBRYO` is intent
+ (a human just did this). Both exist; they answer different
+ questions.
+5. **The brain doesn't move hardware.** All hardware action goes
+ through tools that go through the device layer that goes through
+ ophyd that goes through MMCore. Shadow candidates are constructively
+ prevented from acting. Layers are not negotiable.
+6. **No SaveCardSettings.** Firmware persistent state silently inherits
+ between sessions; if it ever gets out of sync with code it's a
+ debugging nightmare. Apply firmware config every boot, code wins.
+7. **Localhost is the diSPIM box. Remote is view-only by default.**
+ Auth surface stays tiny and explicit. Token upgrade is the seam,
+ not user accounts.
+
+---
+
+## 12. Open Questions (Worth Revisiting Later)
+
+* **Continuous vs episodic shadows.** Continuous always-on shadow
+ captures divergence over time but multiplies cost (multiple LLM
+ candidates running). Episodic shadow at decision moments is cheaper
+ but misses timing-sensitive cases. Hybrid?
+* **Is the conversation history the right substrate at all?** With
+ decision moments hours apart, prior chat may be more distracting
+ than useful. Maybe the brain shouldn't see chat history beyond N
+ hours; the world model + events digest are the durable memory and
+ chat is just for the active dialogue.
+* **How much should the operator know about the orchestrator's plan?**
+ Today the operator drives by asking. With autonomous mode, the
+ orchestrator runs experiments largely on its own. Should there be a
+ permanent "what is the orchestrator thinking right now" surface
+ visible on the Map? An always-on intent display?
+* **Failure semantics.** If a candidate would have made a different
+ decision than production, and production's decision led to a bad
+ outcome, the candidate "wins." How do we score? Define "bad outcome"
+ rigorously enough that it can be measured?
+
+These are not blockers. They are notes for the next iteration of this
+document, after a few weeks of running on the substrate built here.
diff --git a/docs/EVAL.md b/docs/EVAL.md
new file mode 100644
index 00000000..73f35b69
--- /dev/null
+++ b/docs/EVAL.md
@@ -0,0 +1,187 @@
+
+
+> **Status:** design + intended usage for the `gently/eval/` capture/replay substrate and the
+> proposed offline replay harness for testing agentic orchestrator patterns. Grounded in the
+> code as of the 0.22 epoch; the harness itself is a work-in-progress (see the incremental plan).
+
+# Testing agentic orchestrator patterns offline (replay harness)
+
+## Goal
+
+We want to iterate on the agent's design — its realtime reasoning and the wake-router that
+turns developmental events into autonomous turns — **without booking a live microscope run**.
+Concretely: take a recorded session, simulate the microscope conditions from its on-disk
+artifacts (captured events, recorded volumes, recorded perception traces), drive the *real*
+`WakeRouter -> run_wake_turn -> Claude` loop offline, and observe/diff what the agent decides.
+This lets us tune wake triggers, coalescing/throttling, prompt construction, and tool policy on
+a laptop, replayed at a controllable clock (e.g. 10x), instead of waiting hours for embryos to
+develop on a live rig.
+
+## What's already in place (reuse)
+
+A real, tested replay/eval substrate shipped in the 0.22 epoch (`gently/eval/`), plus production
+capture wiring. None of this is hypothetical — it's on disk and runnable today:
+
+- **`gently/eval/event_capture.py`** — `EventCapture` wildcard-subscribes the bus and appends every
+ `Event.to_dict()` to `{session_dir}/events.jsonl`. Auto-wired into **every** live session by
+ `gently/app/agent.py` `_init_event_capture()` (line ~506, called at agent init). Skips only
+ `_NO_HISTORY_TYPES` (`DEVICE_STATE_UPDATE`, `BOTTOM_CAMERA_FRAME`, `LOG_RECORD`); `DETECTOR_EVALUATED`
+ and lifecycle events are **not** skipped.
+- **`gently/eval/event_replay.py`** — `EventReplay(path).replay(target_bus, real_time=, time_scale=, on_event=)`
+ republishes each event via `target.publish_event(ev)`, **preserving the original `Event.timestamp`**
+ (not re-stamping `now()`). `real_time=True` sleeps `(ev.timestamp - prev)/time_scale` between events,
+ so cadence is reproducible. `event_types()` gives a pre-flight histogram.
+- **`gently/eval/shadow.py` + `candidates.py`** — `ShadowRunner` + `OrchestratorCandidate` host
+ sandboxed rule-based candidates (e.g. `ReactiveCandidate`) that may *only* write a `DecisionLog`.
+- **`gently/eval/decision_log.py`** — `Decision`/`DecisionLog` + `prompt_hash()` (sha256[:16] over
+ system prompt + messages) for apples-to-apples A/B diffing.
+- **`scripts/replay_session.py`** — working CLI: resolves a session by id-prefix via
+ `FileStore.list_sessions`, prints `--histogram`, or replays `events.jsonl` into a **fresh** `EventBus()`
+ with an optional `NoOpCandidate`.
+- **Recorded perception inputs/outputs** persist via `FileStore` (`gently/core/file_store.py`):
+ `embryos/{id}/volumes/t{NNNN}.tif` + `.meta.yaml`, `projections/t{NNNN}.jpg`,
+ `predictions.jsonl`, and `traces/t{NNNN}.json` (verbatim `predicted_stage`/`reasoning`/`raw_response`/`stability`).
+ Verified on disk: session `68e7dc33` has 9 embryos, 56 predictions on embryo_001, volume
+ `t0001.tif` shape `[50,512,2048]` uint16.
+- **`timeline.jsonl`** (durable, predates eval) carries 256 `detection/evaluated` records on `68e7dc33`
+ with exactly the fields `WakeRouter._is_wake_worthy` reads (`embryo_id`, `timepoint`,
+ `detector_name`, `stage`, `reasoning`) — a fallback event source for pre-eval-scaffold sessions.
+
+### The one central wiring gap
+
+The real agent subscribes its `WakeRouter` to the **global singleton** bus
+(`gently/app/agent.py:126` `self._event_bus = get_event_bus()`; WakeRouter built with that same bus).
+But `scripts/replay_session.py:124` replays into a **fresh** `EventBus()` that the agent never sees.
+**So today's replay reaches shadow candidates but never the real WakeRouter/agent.** Bridging this —
+either `set_event_bus(replay_bus)` before constructing the agent, or replaying into `get_event_bus()`
+directly — is the core seam to build.
+
+## Approaches, compared
+
+### (A) Event-stream replay into the agent's bus — *recommended first*
+Publish recorded `DETECTOR_EVALUATED` + critical events (`HATCHING_DETECTED`, `EMBRYO_TERMINATED`,
+`ERROR_OCCURRED`, …) onto the bus the agent's `WakeRouter` is subscribed to, on a controllable clock.
+
+- **Reuses:** `EventReplay`, `EventCapture` output, the entire real `WakeRouter` (`_is_wake_worthy`
+ filter at wake_router.py:129, coalesce `COALESCE_WINDOW=20s`, throttle `MIN_WAKE_INTERVAL=120s`,
+ `_flush -> agent.run_wake_turn`).
+- **Fidelity:** Exercises the *real* wake path end-to-end: filtering, transition gate, coalescing,
+ throttling, prompt build, and a real Claude turn (`run_wake_turn -> handle_message_stream`, gated on
+ `agent.mode=='run'`). Highest leverage for the least new code.
+- **Effort:** Medium. Needs (1) the bus bridge above; (2) a running asyncio loop so the async dispatch
+ + `loop.call_later` coalesce timers fire (`EventReplay.replay` is a blocking `time.sleep` loop — run it
+ in a thread or port it to `await asyncio.sleep`, and call `bus.set_event_loop(loop)`); (3) a stub
+ client so any tools the woken agent calls don't hit hardware (autonomous mode already refuses
+ irreversible tools via `_autonomous_active`).
+- **Can't catch:** Anything depending on *fresh* perception of new pixels — the wake note embeds
+ `build_perception_snapshot(agent.perceiver, ...)`, which reads **live** Perceiver state, so this
+ approach needs (B) to make that snapshot reflect the replayed timepoint rather than empty state.
+- **Blocker today:** No recorded session yet contains `DETECTOR_EVALUATED` (verified: all 20 captured
+ `events.jsonl` hold only `STATUS_CHANGED`/`EMBRYO_DETECTED`/`EMBRYOS_UPDATE`). Either capture one fresh
+ perception-driven session, or synthesize `DETECTOR_EVALUATED` events from `traces/`+`timeline.jsonl`.
+
+### (B) Perceiver stub — feed recorded traces
+Replace `agent.perceiver`/`orchestrator.perceiver` with a duck-typed stub whose `__call__(...)` returns
+`.stage`/`.reasoning` from `traces/t{NNNN}.json`, and whose `get_session(embryo_id)` returns an object
+with `.stability`/`.summary()` matching what `build_perception_snapshot` reads
+(`current_stage`/`stability`/`temporal`/`stage_sequence`).
+
+- **Reuses:** All downstream code in `_run_perception` (DETECTOR_EVALUATED emit, trace write,
+ `store_prediction`, `_check_interval_rules`) is pure local code; the Perceiver is the *only* external
+ VLM dependency. `perceiver` is already an optional ctor arg (timelapse.py:71).
+- **Fidelity:** Reproduces recorded perception verbatim — no VLM spend, deterministic. Makes (A)'s wake
+ snapshot reflect the replayed timepoint.
+- **Effort:** Low-medium (one stub class).
+- **Can't catch:** Perception on *new* conditions — it only echoes what the recorded run already saw.
+ Also the stub interface was inferred from call sites (`templates.py` `build_perception_snapshot`,
+ `timelapse.py` `_run_perception`), not from `gently_perception` source — **verify against the
+ installed package** before relying on it.
+
+### (C) Full offline re-feed through the timelapse loop
+Inject a fake `microscope_client` whose `acquire_volume(...)` returns
+`{'success': True, 'volume': diSPIM Room Light
+SwitchBot Bot · __ADDRESS__
+
+ —
+
+
+
+
+
+
+
+
+
+
+ fast
+
+ slow
+ 0.7s
+
+
+
+
+
+
+
+
+
diff --git a/docs/superpowers/notes/2026-06-28-lightsheet-fps-measurement.md b/docs/superpowers/notes/2026-06-28-lightsheet-fps-measurement.md
new file mode 100644
index 00000000..d1a4abd9
--- /dev/null
+++ b/docs/superpowers/notes/2026-06-28-lightsheet-fps-measurement.md
@@ -0,0 +1,57 @@
+# Lightsheet live-view FPS measurement + transport decision (B1 Task 7)
+
+Status: **deferred to the rig** — the numbers below must be filled in on the microscope
+(the streamer needs the real `pymmcore` core, SPIM camera, and rpyc transport; it cannot run
+on the Linux dev box). This file is the measurement protocol + the decision gate.
+
+## What to measure
+
+With the Manual view open and lightsheet live running, record three rates:
+
+| Metric | Where to read it |
+|---|---|
+| **device grab rate** (frames peeked/encoded /s) | device-layer log / instrument `_lightsheet_streamer` |
+| **delivered rate** (frames broadcast /s) | device-layer `_broadcast_lightsheet` |
+| **browser paint rate** | the Manual-view FPS readout (`computeLightsheetFps`) |
+
+Record at two resolution/quality settings:
+- default **512 px / JPEG q70** (`_ls_target_max_dim=512`, `_ls_jpeg_quality=70`)
+- reduced **384 px / q60**
+
+Note the exposure used (the peek floor is `max(exposure, 1/30 s)`).
+
+## Target
+
+**≥ ~15 fps usable for focus** (stretch 25–30), end-to-end latency < ~150 ms.
+
+## Diagnosis rule (the gate)
+
+- **device grab < target** → limiter is exposure / readout / rpyc, **not** transport. Tune
+ exposure, the 512 px size, and JPEG quality. A binary transport path will NOT help — stop here.
+- **device grab ≥ target but browser paint < target** → transport is the bottleneck → build the
+ **binary WebSocket path** (below).
+
+## Conditional escalation — binary WebSocket path
+
+Only if the diagnosis points to transport. The current path is base64-JPEG-in-JSON over SSE →
+EventBus → `ConnectionManager.broadcast` (`json.dumps` + `send_text`) — `connection_manager.py`
+has **no `send_bytes` path** (confirmed). Escalation, on the **agent→browser hop** (where cost
+multiplies per client):
+
+- push raw JPEG bytes via `websocket.send_bytes(prefix + jpeg)` (a 1-byte type tag identifies a
+ lightsheet frame), bypassing **base64 (+33%)**, the **per-client `json.dumps`**, and the
+ **EventBus fan-out**;
+- browser `onmessage` (binary) → `createImageBitmap(new Blob([buf]))` → `ctx.drawImage`;
+- the device→agent SSE stays as-is (single consumer = the monitor, so its base64 cost is paid
+ once, not per browser).
+
+Re-measure after building; record the before/after numbers here.
+
+## Results (fill in on the rig)
+
+| setting | device grab fps | delivered fps | browser paint fps | exposure | notes |
+|---|---|---|---|---|---|
+| 512px/q70 | _TBD_ | _TBD_ | _TBD_ | _TBD_ | |
+| 384px/q60 | _TBD_ | _TBD_ | _TBD_ | _TBD_ | |
+
+Decision: _TBD (transport bottleneck? build binary path Y/N)_
diff --git a/docs/superpowers/plans/2026-06-16-notebook-foundation.md b/docs/superpowers/plans/2026-06-16-notebook-foundation.md
new file mode 100644
index 00000000..4d6a480c
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-16-notebook-foundation.md
@@ -0,0 +1,645 @@
+# Notebook Foundation Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Build the unit-testable foundation of the shared lab notebook — the unified `Note` model and a file-backed `NotebookStore` (write / read / scope-query / rebuildable reverse-indexes / link & supersede) — with no UI, API, or agent wiring.
+
+**Architecture:** A new self-contained module `gently/harness/memory/notebook.py`. One `Note` dataclass (three kinds: observation/finding/question) with author, status, confidence, scope facets (strains/embryos/sessions/threads), typed links, basis, and artifact pointers — orthogonal fields, not subtypes. `NotebookStore` persists one YAML per note under `notebook/notes/{id}_{slug}.yaml` (atomic write, mirroring `FileContextStore`), maintains rebuildable reverse-indexes by strain/embryo/thread, and answers scope+kind+status queries. This is Increment 1's keystone from the design doc (`docs/superpowers/specs/2026-06-16-shared-lab-notebook-design.md`).
+
+**Tech Stack:** Python 3.11, dataclasses, `str`-Enums, PyYAML, pytest (fixtures in `tests/conftest.py`, flat `tests/` layout).
+
+**Follow-on plans (NOT in scope here):** producer wiring (`apply_updates` → notebook), `/api/notebook` + Notebook tab (UI), retrieval/embeddings + brainstorm. Each ships on top of this foundation.
+
+---
+
+### Task 1: The `Note` model
+
+**Files:**
+- Create: `gently/harness/memory/notebook.py`
+- Test: `tests/test_notebook_store.py`
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# tests/test_notebook_store.py
+"""Tests for the shared lab notebook: Note model + NotebookStore."""
+
+from datetime import datetime
+
+from gently.harness.memory.model import Confidence
+from gently.harness.memory.notebook import (
+ Author,
+ Note,
+ NoteKind,
+ NoteStatus,
+ note_from_dict,
+ note_to_dict,
+)
+
+
+class TestNoteModel:
+ def test_round_trip_minimal(self):
+ n = Note(id="abc123", kind=NoteKind.OBSERVATION, body="dim rings at 10 ms")
+ d = note_to_dict(n)
+ assert d["kind"] == "observation"
+ assert d["author"] == "agent" # default
+ assert d["status"] == "confirmed" # default
+ back = note_from_dict(d)
+ assert back == n
+
+ def test_round_trip_full(self):
+ n = Note(
+ id="def456",
+ kind=NoteKind.FINDING,
+ body="temperature shifts timing ~12 min/degC",
+ author=Author.AGENT,
+ title="Temp shifts timing",
+ status=NoteStatus.PROPOSED,
+ confidence=Confidence.MEDIUM,
+ strains=["N2", "OH904"],
+ embryos=["emb_0007"],
+ sessions=["20260615_1432_x"],
+ threads=["q_division_temp"],
+ basis=["obs_1", "obs_2"],
+ links=[{"rel": "supports", "to": "q_division_temp"}],
+ artifacts=[{"kind": "projection", "session": "s1", "embryo": "emb_0007", "t": 42}],
+ created_at=datetime(2026, 6, 16, 11, 0, 0),
+ updated_at=datetime(2026, 6, 16, 11, 0, 0),
+ )
+ back = note_from_dict(note_to_dict(n))
+ assert back == n
+ assert note_to_dict(n)["confidence"] == "medium"
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNoteModel -v`
+Expected: FAIL — `ModuleNotFoundError: No module named 'gently.harness.memory.notebook'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+```python
+# gently/harness/memory/notebook.py
+"""
+The shared lab notebook — unified memory entry (Note) and file-backed store.
+
+One Note kind taxonomy (observation / finding / question); everything else
+(author, status, confidence, scope, links) is an orthogonal field. See
+docs/superpowers/specs/2026-06-16-shared-lab-notebook-design.md.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
+from typing import Any
+
+from .model import Confidence
+
+
+class NoteKind(str, Enum):
+ OBSERVATION = "observation" # immutable record of what was seen/done/read/noted
+ FINDING = "finding" # revisable, supersedable believed claim
+ QUESTION = "question" # open inquiry; large ones are the thread spine
+
+
+class Author(str, Enum):
+ HUMAN = "human"
+ AGENT = "agent"
+ PERCEPTION = "perception"
+
+
+class NoteStatus(str, Enum):
+ OPEN = "open" # questions not yet answered
+ PROPOSED = "proposed" # agent-drafted finding awaiting human confirm
+ CONFIRMED = "confirmed" # accepted observation/finding (default)
+ ANSWERED = "answered" # question resolved
+ SUPERSEDED = "superseded" # replaced by a newer note
+
+
+@dataclass
+class Note:
+ id: str
+ kind: NoteKind
+ body: str
+ author: Author = Author.AGENT
+ title: str | None = None
+ status: NoteStatus = NoteStatus.CONFIRMED
+ confidence: Confidence | None = None
+ strains: list[str] = field(default_factory=list)
+ embryos: list[str] = field(default_factory=list)
+ sessions: list[str] = field(default_factory=list)
+ threads: list[str] = field(default_factory=list)
+ basis: list[str] = field(default_factory=list) # note ids this rests on
+ links: list[dict] = field(default_factory=list) # [{"rel": ..., "to": ...}]
+ artifacts: list[dict] = field(default_factory=list) # FileStore pointers
+ superseded_by: str | None = None
+ created_at: datetime = field(default_factory=datetime.now)
+ updated_at: datetime = field(default_factory=datetime.now)
+
+
+def note_to_dict(n: Note) -> dict[str, Any]:
+ return {
+ "id": n.id,
+ "kind": n.kind.value,
+ "body": n.body,
+ "author": n.author.value,
+ "title": n.title,
+ "status": n.status.value,
+ "confidence": n.confidence.value if n.confidence else None,
+ "strains": list(n.strains),
+ "embryos": list(n.embryos),
+ "sessions": list(n.sessions),
+ "threads": list(n.threads),
+ "basis": list(n.basis),
+ "links": list(n.links),
+ "artifacts": list(n.artifacts),
+ "superseded_by": n.superseded_by,
+ "created_at": n.created_at.isoformat(),
+ "updated_at": n.updated_at.isoformat(),
+ }
+
+
+def note_from_dict(d: dict[str, Any]) -> Note:
+ conf = d.get("confidence")
+ return Note(
+ id=d["id"],
+ kind=NoteKind(d["kind"]),
+ body=d.get("body", ""),
+ author=Author(d.get("author", "agent")),
+ title=d.get("title"),
+ status=NoteStatus(d.get("status", "confirmed")),
+ confidence=Confidence(conf) if conf else None,
+ strains=list(d.get("strains") or []),
+ embryos=list(d.get("embryos") or []),
+ sessions=list(d.get("sessions") or []),
+ threads=list(d.get("threads") or []),
+ basis=list(d.get("basis") or []),
+ links=list(d.get("links") or []),
+ artifacts=list(d.get("artifacts") or []),
+ superseded_by=d.get("superseded_by"),
+ created_at=datetime.fromisoformat(d["created_at"]),
+ updated_at=datetime.fromisoformat(d["updated_at"]),
+ )
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNoteModel -v`
+Expected: PASS (2 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook.py tests/test_notebook_store.py
+git commit -m "feat(notebook): unified Note model + dict serialization"
+```
+
+---
+
+### Task 2: `NotebookStore` — write & read a note
+
+**Files:**
+- Modify: `gently/harness/memory/notebook.py` (append `NotebookStore`)
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+from gently.harness.memory.notebook import NotebookStore
+
+
+class TestNotebookStoreReadWrite:
+ def test_write_assigns_id_and_persists(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ n = Note(id="", kind=NoteKind.OBSERVATION, body="bean stage at t40")
+ note_id = store.write_note(n)
+ assert note_id # non-empty id assigned
+ files = list((tmp_path / "notebook" / "notes").glob("*.yaml"))
+ assert len(files) == 1
+ assert files[0].name.startswith(note_id + "_")
+
+ def test_get_note_round_trip(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ n = Note(id="", kind=NoteKind.FINDING, body="x", strains=["N2"], threads=["t1"])
+ note_id = store.write_note(n)
+ got = store.get_note(note_id)
+ assert got is not None
+ assert got.id == note_id
+ assert got.kind == NoteKind.FINDING
+ assert got.strains == ["N2"]
+
+ def test_get_missing_returns_none(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ assert store.get_note("nope") is None
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookStoreReadWrite -v`
+Expected: FAIL — `ImportError: cannot import name 'NotebookStore'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+```python
+# append to gently/harness/memory/notebook.py
+import copy
+import os
+import re
+import uuid
+from pathlib import Path
+
+import yaml
+
+
+class NotebookStore:
+ """File-backed store for notebook Notes. One YAML per note under notes/;
+ flat pool, rebuildable reverse-indexes (added in Task 3)."""
+
+ def __init__(self, notebook_dir: Path):
+ self.root = Path(notebook_dir)
+ self.notes_dir = self.root / "notes"
+ self.index_dir = self.root / "index"
+ self.notes_dir.mkdir(parents=True, exist_ok=True)
+ self.index_dir.mkdir(parents=True, exist_ok=True)
+
+ # ---- helpers (mirror FileContextStore conventions) ----
+ @staticmethod
+ def _gen_id() -> str:
+ return str(uuid.uuid4())[:8]
+
+ @staticmethod
+ def _slugify(text: str) -> str:
+ slug = re.sub(r"[^a-z0-9]+", "-", (text or "").lower()).strip("-")
+ return slug[:30]
+
+ def _write_yaml(self, path: Path, data: dict) -> None:
+ path.parent.mkdir(parents=True, exist_ok=True)
+ tmp = path.with_suffix(".tmp")
+ with open(tmp, "w", encoding="utf-8") as fh:
+ yaml.safe_dump(data, fh, default_flow_style=False, allow_unicode=True, sort_keys=False)
+ os.replace(str(tmp), str(path))
+
+ def _read_yaml(self, path: Path) -> dict | None:
+ try:
+ with open(path, encoding="utf-8") as fh:
+ return yaml.safe_load(fh)
+ except OSError:
+ return None
+
+ def _note_path(self, note_id: str) -> Path | None:
+ return next(self.notes_dir.glob(f"{note_id}_*.yaml"), None)
+
+ # ---- read/write ----
+ def write_note(self, note: Note) -> str:
+ if not note.id:
+ note.id = self._gen_id()
+ note.updated_at = datetime.now()
+ slug = self._slugify(note.title or note.body or note.kind.value)
+ # remove any stale file for this id (slug may have changed)
+ old = self._note_path(note.id)
+ if old is not None:
+ old.unlink()
+ self._write_yaml(self.notes_dir / f"{note.id}_{slug}.yaml", note_to_dict(note))
+ return note.id
+
+ def get_note(self, note_id: str) -> Note | None:
+ path = self._note_path(note_id)
+ if path is None:
+ return None
+ data = self._read_yaml(path)
+ return note_from_dict(data) if data else None
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookStoreReadWrite -v`
+Expected: PASS (3 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook.py tests/test_notebook_store.py
+git commit -m "feat(notebook): NotebookStore write_note/get_note with atomic YAML"
+```
+
+---
+
+### Task 3: Reverse-indexes (by strain / embryo / thread) + rebuild
+
+**Files:**
+- Modify: `gently/harness/memory/notebook.py` (`NotebookStore`)
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+class TestNotebookIndex:
+ def test_index_updated_on_write(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ a = store.write_note(Note(id="", kind=NoteKind.OBSERVATION, body="a", strains=["N2"]))
+ b = store.write_note(Note(id="", kind=NoteKind.OBSERVATION, body="b", strains=["N2", "OH904"]))
+ assert set(store.ids_for_strain("N2")) == {a, b}
+ assert store.ids_for_strain("OH904") == [b]
+ assert store.ids_for_strain("missing") == []
+
+ def test_index_by_embryo_and_thread(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ a = store.write_note(Note(id="", kind=NoteKind.FINDING, body="a",
+ embryos=["e1"], threads=["t1"]))
+ assert store.ids_for_embryo("e1") == [a]
+ assert store.ids_for_thread("t1") == [a]
+
+ def test_rebuild_index_from_disk(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ a = store.write_note(Note(id="", kind=NoteKind.OBSERVATION, body="a", strains=["N2"]))
+ # a fresh store over the same dir must rebuild the index by scanning notes/
+ store2 = NotebookStore(tmp_path / "notebook")
+ assert store2.ids_for_strain("N2") == [a]
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookIndex -v`
+Expected: FAIL — `AttributeError: 'NotebookStore' object has no attribute 'ids_for_strain'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+Modify `NotebookStore.__init__` to add index state + rebuild, extend `write_note` to update the index, and add the index methods. Replace the existing `__init__` and `write_note` with these versions and add the new methods:
+
+```python
+ # --- replace __init__ ---
+ def __init__(self, notebook_dir: Path):
+ self.root = Path(notebook_dir)
+ self.notes_dir = self.root / "notes"
+ self.index_dir = self.root / "index"
+ self.notes_dir.mkdir(parents=True, exist_ok=True)
+ self.index_dir.mkdir(parents=True, exist_ok=True)
+ # reverse indexes: facet -> {value: [note_id, ...]}
+ self._index: dict[str, dict[str, list[str]]] = {
+ "strain": {}, "embryo": {}, "thread": {}
+ }
+ self.rebuild_index()
+
+ # --- add: facet extraction + index maintenance ---
+ _FACETS = {"strain": "strains", "embryo": "embryos", "thread": "threads"}
+
+ def _index_note(self, note: Note) -> None:
+ for facet, attr in self._FACETS.items():
+ for value in getattr(note, attr):
+ bucket = self._index[facet].setdefault(value, [])
+ if note.id not in bucket:
+ bucket.append(note.id)
+
+ def rebuild_index(self) -> None:
+ """Rebuild reverse-indexes by scanning notes/ (the notes are authoritative;
+ indexes are disposable caches)."""
+ self._index = {"strain": {}, "embryo": {}, "thread": {}}
+ for f in sorted(self.notes_dir.glob("*.yaml")):
+ data = self._read_yaml(f)
+ if data:
+ self._index_note(note_from_dict(data))
+
+ def ids_for_strain(self, strain: str) -> list[str]:
+ return list(self._index["strain"].get(strain, []))
+
+ def ids_for_embryo(self, embryo: str) -> list[str]:
+ return list(self._index["embryo"].get(embryo, []))
+
+ def ids_for_thread(self, thread: str) -> list[str]:
+ return list(self._index["thread"].get(thread, []))
+```
+
+Then add an index-update at the end of `write_note` (just before `return note.id`):
+
+```python
+ self._index_note(note)
+ return note.id
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookIndex -v`
+Expected: PASS (3 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook.py tests/test_notebook_store.py
+git commit -m "feat(notebook): rebuildable reverse-indexes by strain/embryo/thread"
+```
+
+---
+
+### Task 4: `query_notes` — filter by kind / author / status / scope
+
+**Files:**
+- Modify: `gently/harness/memory/notebook.py` (`NotebookStore`)
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+class TestNotebookQuery:
+ def _seed(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ store.write_note(Note(id="o1", kind=NoteKind.OBSERVATION, body="o", strains=["N2"]))
+ store.write_note(Note(id="f1", kind=NoteKind.FINDING, body="f",
+ status=NoteStatus.PROPOSED, strains=["N2"], threads=["t1"]))
+ store.write_note(Note(id="q1", kind=NoteKind.QUESTION, body="q",
+ status=NoteStatus.OPEN, threads=["t1"]))
+ return store
+
+ def test_query_by_kind(self, tmp_path):
+ store = self._seed(tmp_path)
+ ids = {n.id for n in store.query_notes(kind=NoteKind.FINDING)}
+ assert ids == {"f1"}
+
+ def test_query_by_thread_scope(self, tmp_path):
+ store = self._seed(tmp_path)
+ ids = {n.id for n in store.query_notes(thread="t1")}
+ assert ids == {"f1", "q1"}
+
+ def test_query_by_thread_and_kind(self, tmp_path):
+ store = self._seed(tmp_path)
+ ids = {n.id for n in store.query_notes(thread="t1", kind=NoteKind.QUESTION)}
+ assert ids == {"q1"}
+
+ def test_query_by_status(self, tmp_path):
+ store = self._seed(tmp_path)
+ ids = {n.id for n in store.query_notes(status=NoteStatus.OPEN)}
+ assert ids == {"q1"}
+
+ def test_query_all_sorted_newest_first(self, tmp_path):
+ store = self._seed(tmp_path)
+ notes = store.query_notes()
+ assert len(notes) == 3
+ ts = [n.created_at for n in notes]
+ assert ts == sorted(ts, reverse=True)
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookQuery -v`
+Expected: FAIL — `AttributeError: 'NotebookStore' object has no attribute 'query_notes'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+```python
+# add to NotebookStore
+ def query_notes(
+ self,
+ *,
+ kind: NoteKind | None = None,
+ author: Author | None = None,
+ status: NoteStatus | None = None,
+ strain: str | None = None,
+ embryo: str | None = None,
+ thread: str | None = None,
+ ) -> list[Note]:
+ """Structural query: narrow by scope via the indexes, then filter by
+ kind/author/status. Returned newest-first. (No semantic ranking here —
+ that's a later increment.)"""
+ # 1. candidate ids — intersect any scope facets given, else all notes
+ scope_sets: list[set[str]] = []
+ if strain is not None:
+ scope_sets.append(set(self.ids_for_strain(strain)))
+ if embryo is not None:
+ scope_sets.append(set(self.ids_for_embryo(embryo)))
+ if thread is not None:
+ scope_sets.append(set(self.ids_for_thread(thread)))
+ if scope_sets:
+ candidate_ids: set[str] | None = set.intersection(*scope_sets)
+ else:
+ candidate_ids = None # means "all"
+
+ # 2. load + filter
+ results: list[Note] = []
+ if candidate_ids is not None:
+ notes = [n for n in (self.get_note(i) for i in candidate_ids) if n]
+ else:
+ notes = [
+ note_from_dict(d)
+ for d in (self._read_yaml(f) for f in self.notes_dir.glob("*.yaml"))
+ if d
+ ]
+ for n in notes:
+ if kind is not None and n.kind != kind:
+ continue
+ if author is not None and n.author != author:
+ continue
+ if status is not None and n.status != status:
+ continue
+ results.append(n)
+ results.sort(key=lambda n: n.created_at, reverse=True)
+ return results
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookQuery -v`
+Expected: PASS (5 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook.py tests/test_notebook_store.py
+git commit -m "feat(notebook): query_notes by kind/author/status/scope"
+```
+
+---
+
+### Task 5: `link_notes` and `supersede_note`
+
+**Files:**
+- Modify: `gently/harness/memory/notebook.py` (`NotebookStore`)
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+class TestNotebookLinkSupersede:
+ def test_link_notes_adds_typed_edge(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ a = store.write_note(Note(id="", kind=NoteKind.FINDING, body="a"))
+ b = store.write_note(Note(id="", kind=NoteKind.QUESTION, body="b"))
+ store.link_notes(a, "supports", b)
+ got = store.get_note(a)
+ assert {"rel": "supports", "to": b} in got.links
+
+ def test_supersede_marks_old_and_points_new(self, tmp_path):
+ store = NotebookStore(tmp_path / "notebook")
+ old = store.write_note(Note(id="", kind=NoteKind.FINDING, body="old claim"))
+ new = store.write_note(Note(id="", kind=NoteKind.FINDING, body="better claim"))
+ store.supersede_note(old, new)
+ old_n = store.get_note(old)
+ new_n = store.get_note(new)
+ assert old_n.status == NoteStatus.SUPERSEDED
+ assert old_n.superseded_by == new
+ assert {"rel": "refines", "to": old} in new_n.links
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestNotebookLinkSupersede -v`
+Expected: FAIL — `AttributeError: 'NotebookStore' object has no attribute 'link_notes'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+```python
+# add to NotebookStore
+ def link_notes(self, from_id: str, rel: str, to_id: str) -> None:
+ """Add a typed edge from one note to another (append-only)."""
+ note = self.get_note(from_id)
+ if note is None:
+ raise KeyError(from_id)
+ edge = {"rel": rel, "to": to_id}
+ if edge not in note.links:
+ note.links.append(edge)
+ self.write_note(note)
+
+ def supersede_note(self, old_id: str, new_id: str) -> None:
+ """Mark old as superseded (kept, never deleted) and link the new note
+ back to it as a refinement — the chain is the intellectual history."""
+ old = self.get_note(old_id)
+ if old is None:
+ raise KeyError(old_id)
+ old.status = NoteStatus.SUPERSEDED
+ old.superseded_by = new_id
+ self.write_note(old)
+ self.link_notes(new_id, "refines", old_id)
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py -v`
+Expected: PASS (all tests across all classes pass)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook.py tests/test_notebook_store.py
+git commit -m "feat(notebook): link_notes + supersede_note (append-only history)"
+```
+
+---
+
+## Self-Review
+
+**Spec coverage (against the design doc §2 data model):**
+- Three kinds (Observation/Finding/Question) → Task 1 `NoteKind`. ✓
+- Orthogonal fields (author/status/confidence/scope/links/artifacts) → Task 1 `Note`. ✓
+- Flat note pool + rebuildable reverse-indexes (strain/embryo/thread) → Tasks 2-3. ✓
+- "By question + by strain + links coexist over flat YAML, no DB" → Tasks 3-4 (indexes + scope-intersect query). ✓
+- Append-only / supersede-never-overwrite → Task 5 `supersede_note`. ✓
+- Typed links graph → Tasks 1 (`links`) + 5 (`link_notes`). ✓
+- *Deferred to follow-on plans (correctly out of scope):* inquiry-thread object, working-memory split, producer wiring, API/tab, embeddings/retrieval, consolidation. Noted in header.
+
+**Placeholder scan:** No TBD/TODO; every code step shows complete code; commands have expected output. ✓
+
+**Type consistency:** `Note`, `NoteKind`, `Author`, `NoteStatus`, `note_to_dict`/`note_from_dict`, and `NotebookStore.{write_note,get_note,rebuild_index,ids_for_strain,ids_for_embryo,ids_for_thread,query_notes,link_notes,supersede_note}` are named identically across all tasks and tests. `Confidence` is imported from `.model` (confirmed to exist). ✓
diff --git a/docs/superpowers/plans/2026-06-16-notebook-live-edge.md b/docs/superpowers/plans/2026-06-16-notebook-live-edge.md
new file mode 100644
index 00000000..9265d769
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-16-notebook-live-edge.md
@@ -0,0 +1,188 @@
+# Notebook Live Edge Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: superpowers:executing-plans. Steps use checkbox (`- [ ]`) syntax.
+
+**Goal:** Make the Home "Agent's view" panel surface recent notebook activity — the ambient "live edge" of the notebook (design §3, two-faced presentation), complementing the Notebook tab (the reading room).
+
+**Architecture:** Add an optional `limit` to the notes read API (TDD). Extend `context-surface.js` to also fetch recent notes and render a "From the notebook" section whose rows click through to the Notebook tab. Reuse the existing `cx-dot` colors (amber/blue/green) for kinds — no new CSS.
+
+**Tech Stack:** FastAPI, pytest + TestClient (venv), vanilla JS.
+
+**Out of scope:** retrieval/"Ask the notebook" (next increment); proactive surfacing.
+
+---
+
+### Task 1: `limit` param on `GET /api/notebook/notes`
+
+**Files:**
+- Modify: `gently/ui/web/routes/notebook.py`
+- Test: `tests/test_notebook_api.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_api.py
+class TestLimit:
+ def test_limit_returns_newest(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ data = client.get("/api/notebook/notes?limit=1").json()
+ assert len(data["notes"]) == 1 # newest-first, capped
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_api.py::TestLimit -v`
+Expected: FAIL — returns 2 notes, not 1.
+
+- [ ] **Step 3: Write minimal implementation**
+
+In `gently/ui/web/routes/notebook.py`, add a `limit` param to `list_notes` and slice. Replace the `list_notes` signature and the final return:
+
+```python
+ @router.get("/api/notebook/notes")
+ async def list_notes(
+ kind: str | None = None,
+ author: str | None = None,
+ status: str | None = None,
+ strain: str | None = None,
+ embryo: str | None = None,
+ thread: str | None = None,
+ limit: int | None = None,
+ ):
+ nb = _nb()
+ if nb is None:
+ return {"available": False, "notes": []}
+ notes = nb.query_notes(
+ kind=_coerce(NoteKind, kind),
+ author=_coerce(Author, author),
+ status=_coerce(NoteStatus, status),
+ strain=strain,
+ embryo=embryo,
+ thread=thread,
+ )
+ if limit is not None and limit >= 0:
+ notes = notes[:limit]
+ return {"available": True, "notes": [note_to_dict(n) for n in notes]}
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_api.py -q`
+Expected: all pass
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/routes/notebook.py tests/test_notebook_api.py
+git commit -m "feat(notebook): limit param on GET /api/notebook/notes"
+```
+
+---
+
+### Task 2: "From the notebook" section in the Agent's-view panel
+
+**Files:**
+- Modify: `gently/ui/web/static/js/context-surface.js`
+
+Verification is browser-based (chrome-devtools), not pytest.
+
+- [ ] **Step 1: Extend `fetchAndRender` to also pull recent notes**
+
+Replace `fetchAndRender` with a version that fetches both endpoints and passes notes to `render`:
+
+```javascript
+ async function fetchAndRender() {
+ if (!el || loading) return;
+ loading = true;
+ try {
+ const [ctx, nb] = await Promise.all([
+ fetch('/api/context').then(r => r.json()).catch(() => ({})),
+ fetch('/api/notebook/notes?limit=5').then(r => r.json()).catch(() => ({})),
+ ]);
+ render(ctx || {}, (nb && nb.notes) || []);
+ } catch (e) { /* keep last render */ }
+ finally { loading = false; }
+ }
+```
+
+- [ ] **Step 2: Render the notebook section**
+
+Replace `render(data)` with `render(data, notes)`. Add the notebook section and include notes in the empty-state check. Replace the whole `render` function body:
+
+```javascript
+ function render(data, notes) {
+ if (!el) return;
+ notes = notes || [];
+ const hc = hasControl();
+ const questions = data.questions || [], watchpoints = data.watchpoints || [], expectations = data.expectations || [];
+ el.classList.remove('hidden');
+ if (!questions.length && !watchpoints.length && !expectations.length && !notes.length) {
+ el.innerHTML = 'Gently
+ What do you need today?
+ +
+
+
+
+
+
+
+
+
+ Microscope hardware
+ Start the device layer & connect to the rig · :60610
+
+
+
+
+
+
+
+
+
+
+ AI agent
+ Chat, perception & planning — uses your API key
+ Advanced options · remembers your choice
+ Agent’s view
' +
+ 'Nothing yet — the agent’s notes, expectations, and open questions appear here as it works.
';
+ return;
+ }
+
+ const qHtml = questions.map(it => `
+
+
+ ${esc(it.content)}
+ ${hc ? '' : ''}
+ ${hc ? '' : ''}
+
`).join('');
+ const wHtml = watchpoints.map(it => `
+
+
+ ${esc(it.target)}${it.condition ? ' — ' + esc(it.condition) : ''}
+ ${hc ? '' : ''}
+
`).join('');
+ const eHtml = expectations.map(it => `
+
+
+ ${esc(it.target)}${it.prediction ? ': ' + esc(it.prediction) : ''}
+ ${hc ? '' : ''}
+
`).join('');
+ // kind → existing cx-dot color: observation=blue, finding=green, question=amber
+ const dotFor = (k) => k === 'finding' ? 'cx-e' : (k === 'question' ? 'cx-q' : 'cx-w');
+ const nHtml = notes.map(n => `
+
+
+ ${esc(n.title || n.body)}
+
`).join('');
+
+ el.innerHTML = 'Agent’s view
' +
+ section('Open questions', qHtml) + section('Watching', wHtml) +
+ section('Expectations', eHtml) + section('From the notebook', nHtml);
+ wire();
+ }
+```
+
+- [ ] **Step 3: Make notebook rows click through to the Notebook tab**
+
+In `wire()`, after the existing `.cx-item` loop, add a handler for note rows (append inside `wire`, before its closing brace):
+
+```javascript
+ el.querySelectorAll('.cx-note').forEach(row => {
+ row.style.cursor = 'pointer';
+ row.addEventListener('click', () => {
+ if (typeof switchTab === 'function') switchTab('notebook');
+ });
+ });
+```
+
+- [ ] **Step 4: Verify in the browser**
+
+Restart the server, open `http://localhost:8080`, confirm the Home "Agent's view" panel shows a "From the notebook" section with recent notes, and clicking a row switches to the Notebook tab. Use chrome-devtools (navigate, evaluate_script to click, take_screenshot, list_console_messages → zero errors).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/static/js/context-surface.js
+git commit -m "feat(notebook): Agent's-view live edge — recent notes section -> Notebook tab"
+```
+
+---
+
+## Self-Review
+**Spec coverage:** design §3 two-faced presentation — the ambient live edge now surfaces recent notebook notes on Home, clicking through to the tab. ✓ Reuses `/api/notebook/notes` (+ new `limit`) and existing `cx-dot` colors. ✓
+**Placeholder scan:** none. ✓
+**Type consistency:** `render(data, notes)`, `dotFor`, `limit` param, `switchTab('notebook')` all consistent; kinds map to existing cx classes. ✓
diff --git a/docs/superpowers/plans/2026-06-16-notebook-producer-bridge.md b/docs/superpowers/plans/2026-06-16-notebook-producer-bridge.md
new file mode 100644
index 00000000..d518f621
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-16-notebook-producer-bridge.md
@@ -0,0 +1,251 @@
+# Notebook Producer Bridge Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax.
+
+**Goal:** Make the existing agent-memory write path actually populate the shared notebook — when `FileContextStore.apply_updates()` records observations and learnings, mirror them into the `NotebookStore` as Notes.
+
+**Architecture:** Pure converters (`observation_to_note`, `learning_to_note`) in `notebook.py`; a lazy `FileContextStore.notebook` property rooted at `agent_dir/notebook`; a guarded mirror step at the end of `apply_updates`. Builds on the foundation plan (`2026-06-16-notebook-foundation.md`). Backend-only, no UI/agent-loop changes. Transitional dual-write (legacy YAML + notebook) — legacy silos retire in a later increment.
+
+**Tech Stack:** Python 3.11, dataclasses, PyYAML, pytest (`file_context_store` fixture in `tests/conftest.py`).
+
+**Out of scope:** wiring the live loop to *call* `apply_updates` (separate increment); read API + Notebook tab; mapping expectations/watchpoints (they're working memory, not notebook entries — see design doc §2).
+
+---
+
+### Task 1: Converters — Observation/Learning → Note
+
+**Files:**
+- Modify: `gently/harness/memory/notebook.py`
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+from datetime import datetime as _dt
+
+from gently.harness.memory.model import Learning, Observation
+from gently.harness.memory.notebook import learning_to_note, observation_to_note
+
+
+class TestConverters:
+ def test_observation_to_note(self):
+ obs = Observation(
+ id="o1", timestamp=_dt(2026, 6, 16, 9, 0, 0), type="milestone",
+ content="nerve ring formed", embryo_id="e1", session_id="s1",
+ relates_to=["o0"], gently_refs={"kind": "projection", "t": 42},
+ )
+ n = observation_to_note(obs)
+ assert n.id == "o1"
+ assert n.kind == NoteKind.OBSERVATION
+ assert n.body == "nerve ring formed"
+ assert n.author == Author.AGENT
+ assert n.embryos == ["e1"]
+ assert n.sessions == ["s1"]
+ assert {"rel": "relates_to", "to": "o0"} in n.links
+ assert n.artifacts == [{"kind": "projection", "t": 42}]
+ assert n.created_at == _dt(2026, 6, 16, 9, 0, 0)
+
+ def test_learning_to_note(self):
+ lrn = Learning(id="l1", content="rings form by comma", confidence=Confidence.HIGH)
+ n = learning_to_note(lrn)
+ assert n.id == "l1"
+ assert n.kind == NoteKind.FINDING
+ assert n.body == "rings form by comma"
+ assert n.status == NoteStatus.PROPOSED # agent-drafted, awaits confirm
+ assert n.confidence == Confidence.HIGH
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestConverters -v`
+Expected: FAIL — `ImportError: cannot import name 'observation_to_note'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+Add the model imports and converters to `notebook.py`. Extend the existing model import line:
+
+```python
+from .model import Confidence, Learning, Observation
+```
+
+Append at end of `notebook.py` (module-level functions, after `note_from_dict`):
+
+```python
+def observation_to_note(obs: Observation) -> Note:
+ """Bridge a legacy Observation into a notebook Note (kind=observation)."""
+ return Note(
+ id=obs.id,
+ kind=NoteKind.OBSERVATION,
+ body=obs.content,
+ author=Author.AGENT,
+ embryos=[obs.embryo_id] if obs.embryo_id else [],
+ sessions=[obs.session_id] if obs.session_id else [],
+ links=[{"rel": "relates_to", "to": r} for r in (obs.relates_to or [])],
+ artifacts=[obs.gently_refs] if obs.gently_refs else [],
+ created_at=obs.timestamp,
+ updated_at=obs.timestamp,
+ )
+
+
+def learning_to_note(learning: Learning) -> Note:
+ """Bridge a legacy Learning into a notebook Note (kind=finding, proposed)."""
+ return Note(
+ id=learning.id,
+ kind=NoteKind.FINDING,
+ body=learning.content,
+ author=Author.AGENT,
+ status=NoteStatus.PROPOSED,
+ confidence=learning.confidence,
+ created_at=learning.created_at,
+ updated_at=learning.created_at,
+ )
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestConverters -v`
+Expected: PASS (2 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook.py tests/test_notebook_store.py
+git commit -m "feat(notebook): Observation/Learning -> Note converters"
+```
+
+---
+
+### Task 2: `FileContextStore.notebook` property
+
+**Files:**
+- Modify: `gently/harness/memory/file_store.py` (add property near `apply_updates`)
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+class TestContextStoreNotebook:
+ def test_notebook_property_rooted_under_agent_dir(self, file_context_store):
+ nb = file_context_store.notebook
+ assert nb.root == file_context_store.agent_dir / "notebook"
+
+ def test_notebook_property_is_cached(self, file_context_store):
+ assert file_context_store.notebook is file_context_store.notebook
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestContextStoreNotebook -v`
+Expected: FAIL — `AttributeError: 'FileContextStore' object has no attribute 'notebook'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+In `gently/harness/memory/file_store.py`, add this property immediately **before** `def apply_updates(self, updates: ContextUpdates):` (line ~2178):
+
+```python
+ @property
+ def notebook(self):
+ """The shared lab notebook, rooted at agent_dir/notebook (lazy)."""
+ nb = getattr(self, "_notebook", None)
+ if nb is None:
+ from .notebook import NotebookStore
+ nb = NotebookStore(self.agent_dir / "notebook")
+ self._notebook = nb
+ return nb
+
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestContextStoreNotebook -v`
+Expected: PASS (2 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/file_store.py tests/test_notebook_store.py
+git commit -m "feat(notebook): FileContextStore.notebook lazy property"
+```
+
+---
+
+### Task 3: Mirror observations & learnings in `apply_updates`
+
+**Files:**
+- Modify: `gently/harness/memory/file_store.py` (`apply_updates`)
+- Test: `tests/test_notebook_store.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_store.py
+class TestApplyUpdatesMirror:
+ def test_apply_updates_mirrors_observations_and_learnings(self, file_context_store):
+ from gently.harness.memory.model import ContextUpdates
+
+ cs = file_context_store
+ obs = Observation(id="o1", timestamp=_dt(2026, 6, 16, 9, 0, 0),
+ type="milestone", content="ring formed", embryo_id="e1")
+ lrn = Learning(id="l1", content="rings form by comma", confidence=Confidence.HIGH)
+ cs.apply_updates(ContextUpdates(new_observations=[obs], new_learnings=[lrn]))
+
+ bodies = {n.body for n in cs.notebook.query_notes()}
+ assert "ring formed" in bodies
+ assert "rings form by comma" in bodies
+ assert cs.notebook.ids_for_embryo("e1") == ["o1"]
+
+ def test_apply_updates_empty_is_noop_for_notebook(self, file_context_store):
+ from gently.harness.memory.model import ContextUpdates
+
+ cs = file_context_store
+ cs.apply_updates(ContextUpdates())
+ assert cs.notebook.query_notes() == []
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestApplyUpdatesMirror -v`
+Expected: FAIL — `assert "ring formed" in set()` (notebook not populated yet)
+
+- [ ] **Step 3: Write minimal implementation**
+
+In `gently/harness/memory/file_store.py`, at the END of `apply_updates` (after the `if updates.new_focus is not None:` block), append:
+
+```python
+ # Mirror new observations & learnings into the shared notebook
+ # (best-effort — a notebook failure never breaks the legacy write).
+ from .notebook import learning_to_note, observation_to_note
+
+ try:
+ for obs in updates.new_observations:
+ self.notebook.write_note(observation_to_note(obs))
+ for learning in updates.new_learnings:
+ self.notebook.write_note(learning_to_note(learning))
+ except Exception:
+ logger.warning("notebook mirror failed", exc_info=True)
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_store.py::TestApplyUpdatesMirror -v`
+Expected: PASS (2 passed)
+
+- [ ] **Step 5: Run full notebook suite + commit**
+
+Run: `python -m pytest tests/test_notebook_store.py -q`
+Expected: all pass
+
+```bash
+git add gently/harness/memory/file_store.py tests/test_notebook_store.py
+git commit -m "feat(notebook): apply_updates mirrors observations & learnings into notebook"
+```
+
+---
+
+## Self-Review
+
+**Spec coverage:** Producer wiring (design doc increment 1b) — `apply_updates` now populates the notebook. ✓ Converters honor the model (Observation→observation note, Learning→finding/proposed). ✓ Working-memory types (expectation/watchpoint) intentionally not mirrored (design §2). ✓
+**Placeholder scan:** none; complete code + commands throughout. ✓
+**Type consistency:** `observation_to_note`/`learning_to_note`, `FileContextStore.notebook`, `NoteKind`/`Author`/`NoteStatus`/`Confidence` match the foundation module and `model.py` (`Observation`, `Learning`, `ContextUpdates` confirmed at `file_store.py:2178-2203`). ✓
diff --git a/docs/superpowers/plans/2026-06-16-notebook-read-api.md b/docs/superpowers/plans/2026-06-16-notebook-read-api.md
new file mode 100644
index 00000000..f4a5f20d
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-16-notebook-read-api.md
@@ -0,0 +1,265 @@
+# Notebook Read API Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:executing-plans (or subagent-driven-development) to implement task-by-task. Steps use checkbox (`- [ ]`) syntax.
+
+**Goal:** Expose the shared notebook over HTTP so the frontend can read it — list/filter notes, fetch one note, and list inquiry-threads with counts.
+
+**Architecture:** A new route module `gently/ui/web/routes/notebook.py` exposing `create_router(server)` (the established pattern), reading `server.context_store.notebook` (the `NotebookStore` added in the producer-bridge plan) and serializing via `note_to_dict`. Registered in `routes/__init__.py`. Read-only; authoring/curation is a later increment.
+
+**Tech Stack:** FastAPI `APIRouter`, pytest + `fastapi.testclient.TestClient`, the `file_context_store` fixture (`tests/conftest.py`).
+
+**Out of scope:** the Notebook tab UI (next plan, browser-verified); the Agent's-View rewire; retrieval/embeddings.
+
+---
+
+### Task 1: Route module — list & get notes
+
+**Files:**
+- Create: `gently/ui/web/routes/notebook.py`
+- Modify: `gently/ui/web/routes/__init__.py`
+- Test: `tests/test_notebook_api.py`
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# tests/test_notebook_api.py
+"""Tests for the notebook read API."""
+
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from gently.harness.memory.notebook import Note, NoteKind, NoteStatus
+
+
+def _make_app(context_store):
+ from gently.ui.web.routes.notebook import create_router
+
+ app = FastAPI()
+
+ class _Server:
+ pass
+
+ server = _Server()
+ server.context_store = context_store
+ app.include_router(create_router(server))
+ return app
+
+
+def _seed(cs):
+ nb = cs.notebook
+ nb.write_note(Note(id="o1", kind=NoteKind.OBSERVATION, body="ring formed", strains=["N2"]))
+ nb.write_note(Note(id="f1", kind=NoteKind.FINDING, body="rings by comma",
+ status=NoteStatus.PROPOSED, strains=["N2"], threads=["t1"]))
+ return cs
+
+
+class TestListNotes:
+ def test_no_store_available_false(self):
+ client = TestClient(_make_app(None))
+ data = client.get("/api/notebook/notes").json()
+ assert data == {"available": False, "notes": []}
+
+ def test_list_all(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ data = client.get("/api/notebook/notes").json()
+ assert data["available"] is True
+ assert {n["id"] for n in data["notes"]} == {"o1", "f1"}
+
+ def test_filter_by_kind(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ data = client.get("/api/notebook/notes?kind=finding").json()
+ assert {n["id"] for n in data["notes"]} == {"f1"}
+
+ def test_filter_by_strain(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ data = client.get("/api/notebook/notes?strain=N2").json()
+ assert {n["id"] for n in data["notes"]} == {"o1", "f1"}
+
+ def test_invalid_kind_is_ignored(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ data = client.get("/api/notebook/notes?kind=bogus").json()
+ assert {n["id"] for n in data["notes"]} == {"o1", "f1"}
+
+
+class TestGetNote:
+ def test_get_existing(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ data = client.get("/api/notebook/notes/o1").json()
+ assert data["id"] == "o1"
+ assert data["body"] == "ring formed"
+
+ def test_get_missing_404(self, file_context_store):
+ client = TestClient(_make_app(_seed(file_context_store)))
+ resp = client.get("/api/notebook/notes/nope")
+ assert resp.status_code == 404
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_api.py -v`
+Expected: FAIL — `ModuleNotFoundError: No module named 'gently.ui.web.routes.notebook'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+Create `gently/ui/web/routes/notebook.py`:
+
+```python
+"""Notebook (shared lab notebook) read routes.
+
+Exposes the notebook's Notes for the Notebook tab + Agent's-View live edge.
+Read-only here; authoring/curation come in a later increment.
+"""
+
+from fastapi import APIRouter, HTTPException
+
+from gently.harness.memory.notebook import Author, NoteKind, NoteStatus, note_to_dict
+
+
+def _coerce(enum_cls, value):
+ """Parse a query-param string into an enum; invalid/None → None (no filter)."""
+ if value is None:
+ return None
+ try:
+ return enum_cls(value)
+ except ValueError:
+ return None
+
+
+def create_router(server) -> APIRouter:
+ router = APIRouter()
+
+ def _nb():
+ cs = getattr(server, "context_store", None)
+ return cs.notebook if cs is not None else None
+
+ @router.get("/api/notebook/notes")
+ async def list_notes(
+ kind: str | None = None,
+ author: str | None = None,
+ status: str | None = None,
+ strain: str | None = None,
+ embryo: str | None = None,
+ thread: str | None = None,
+ ):
+ nb = _nb()
+ if nb is None:
+ return {"available": False, "notes": []}
+ notes = nb.query_notes(
+ kind=_coerce(NoteKind, kind),
+ author=_coerce(Author, author),
+ status=_coerce(NoteStatus, status),
+ strain=strain,
+ embryo=embryo,
+ thread=thread,
+ )
+ return {"available": True, "notes": [note_to_dict(n) for n in notes]}
+
+ @router.get("/api/notebook/notes/{note_id}")
+ async def get_note(note_id: str):
+ nb = _nb()
+ if nb is None:
+ raise HTTPException(status_code=404, detail="notebook unavailable")
+ note = nb.get_note(note_id)
+ if note is None:
+ raise HTTPException(status_code=404, detail="note not found")
+ return note_to_dict(note)
+
+ return router
+```
+
+Then register it in `gently/ui/web/routes/__init__.py`. Add the import after the `images` import line:
+
+```python
+from .notebook import create_router as create_notebook_router
+```
+
+And add `create_notebook_router,` to the factory tuple in `register_all_routes` (after `create_context_router,`):
+
+```python
+ create_context_router,
+ create_notebook_router,
+ ):
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_api.py -v`
+Expected: PASS (7 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/routes/notebook.py gently/ui/web/routes/__init__.py tests/test_notebook_api.py
+git commit -m "feat(notebook): read API — GET /api/notebook/notes + /notes/{id}"
+```
+
+---
+
+### Task 2: `GET /api/notebook/threads`
+
+**Files:**
+- Modify: `gently/ui/web/routes/notebook.py`
+- Test: `tests/test_notebook_api.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_api.py
+class TestThreads:
+ def test_no_store(self):
+ client = TestClient(_make_app(None))
+ assert client.get("/api/notebook/threads").json() == {"available": False, "threads": []}
+
+ def test_thread_counts(self, file_context_store):
+ cs = file_context_store
+ nb = cs.notebook
+ nb.write_note(Note(id="a", kind=NoteKind.QUESTION, body="q", threads=["t1"]))
+ nb.write_note(Note(id="b", kind=NoteKind.FINDING, body="f", threads=["t1", "t2"]))
+ client = TestClient(_make_app(cs))
+ data = client.get("/api/notebook/threads").json()
+ assert data["available"] is True
+ assert data["threads"] == [{"id": "t1", "count": 2}, {"id": "t2", "count": 1}]
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `python -m pytest tests/test_notebook_api.py::TestThreads -v`
+Expected: FAIL — 404 (route not defined)
+
+- [ ] **Step 3: Write minimal implementation**
+
+Add this endpoint inside `create_router`, just before `return router`:
+
+```python
+ @router.get("/api/notebook/threads")
+ async def list_threads():
+ nb = _nb()
+ if nb is None:
+ return {"available": False, "threads": []}
+ counts: dict[str, int] = {}
+ for n in nb.query_notes():
+ for t in n.threads:
+ counts[t] = counts.get(t, 0) + 1
+ threads = [{"id": t, "count": c} for t, c in sorted(counts.items())]
+ return {"available": True, "threads": threads}
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `python -m pytest tests/test_notebook_api.py -q`
+Expected: all pass
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/routes/notebook.py tests/test_notebook_api.py
+git commit -m "feat(notebook): read API — GET /api/notebook/threads with counts"
+```
+
+---
+
+## Self-Review
+
+**Spec coverage:** read surface API for the notebook (design increment 1c, backend half) — query notes by kind/author/status/scope, fetch one, list threads. ✓ Reuses `NotebookStore.query_notes` + `note_to_dict` from the foundation. ✓ Follows `create_router(server)` + `server.context_store` convention (`context.py`). ✓
+**Placeholder scan:** none — complete code + commands. ✓
+**Type consistency:** `create_router`, `note_to_dict`, `NoteKind`/`Author`/`NoteStatus`, `server.context_store.notebook`, `nb.query_notes`/`get_note` all match the foundation + producer-bridge modules. Registration matches the existing tuple in `routes/__init__.py`. ✓
diff --git a/docs/superpowers/plans/2026-06-17-notebook-ask.md b/docs/superpowers/plans/2026-06-17-notebook-ask.md
new file mode 100644
index 00000000..c03ea526
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-17-notebook-ask.md
@@ -0,0 +1,408 @@
+# "Ask the Notebook" Implementation Plan (Increment 2, backend)
+
+> **For agentic workers:** REQUIRED SUB-SKILL: superpowers:executing-plans. Steps use checkbox (`- [ ]`) syntax.
+
+**Goal:** Let the notebook be *reasoned with* — given a question (+ optional scope), retrieve relevant Notes, ask Claude to synthesize a **grounded, cited** answer, and return it as a validated structured object.
+
+**Architecture:** A new module `gently/harness/memory/notebook_ask.py`: structural retrieval (`select_notes`) + a forced-`tool_choice` synthesis call (`answer_question`) that reuses gently's conventions — `anthropic.AsyncAnthropic` (per `chat.py:198`), `settings.models.main` (Opus 4.8), structured output via a pinned tool (per `verifier.py`), and **no self-rated confidence** (per the lab rule). A `POST /api/notebook/ask` route wires retrieval → synthesis. The Claude client is injected so everything is unit-testable with a fake.
+
+**Tech Stack:** Python 3.11, `anthropic` SDK, FastAPI, pytest + TestClient (venv).
+
+**Out of scope (later plans):** embeddings/semantic recall (structural-only here); the "Ask" UI box on the Notebook tab; proactive surfacing.
+
+---
+
+### Task 1: Structural retrieval — `select_notes`
+
+**Files:**
+- Create: `gently/harness/memory/notebook_ask.py`
+- Test: `tests/test_notebook_ask.py`
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# tests/test_notebook_ask.py
+"""Tests for 'Ask the notebook' — retrieval + grounded synthesis."""
+
+from gently.harness.memory.notebook import Note, NoteKind
+from gently.harness.memory.notebook_ask import select_notes
+
+
+def _seed(cs):
+ nb = cs.notebook
+ nb.write_note(Note(id="o1", kind=NoteKind.OBSERVATION, body="ring formed", strains=["N2"], threads=["t1"]))
+ nb.write_note(Note(id="f1", kind=NoteKind.FINDING, body="12 min/degC", strains=["N2"], threads=["t1"]))
+ nb.write_note(Note(id="x1", kind=NoteKind.OBSERVATION, body="unrelated", strains=["OH904"]))
+ return nb
+
+
+class TestSelectNotes:
+ def test_scope_by_thread(self, file_context_store):
+ nb = _seed(file_context_store)
+ ids = {n.id for n in select_notes(nb, thread="t1")}
+ assert ids == {"o1", "f1"}
+
+ def test_scope_by_strain(self, file_context_store):
+ nb = _seed(file_context_store)
+ ids = {n.id for n in select_notes(nb, strain="OH904")}
+ assert ids == {"x1"}
+
+ def test_no_scope_returns_recent_capped(self, file_context_store):
+ nb = _seed(file_context_store)
+ notes = select_notes(nb, limit=2)
+ assert len(notes) == 2 # newest-first, capped
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_ask.py::TestSelectNotes -v`
+Expected: FAIL — `ModuleNotFoundError: No module named 'gently.harness.memory.notebook_ask'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+```python
+# gently/harness/memory/notebook_ask.py
+"""Ask the notebook — retrieve relevant Notes and synthesize a grounded,
+cited answer with Claude. Structural retrieval only (semantic recall is a
+later increment). See docs/superpowers/specs/2026-06-16-shared-lab-notebook-design.md §4.
+"""
+
+from __future__ import annotations
+
+from .notebook import Note, NotebookStore
+
+
+def select_notes(
+ store: NotebookStore,
+ *,
+ thread: str | None = None,
+ strain: str | None = None,
+ limit: int = 12,
+) -> list[Note]:
+ """Structural narrowing: scope by thread/strain when given, else recent.
+ Returns newest-first, capped at `limit`."""
+ notes = store.query_notes(thread=thread, strain=strain)
+ return notes[:limit]
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_ask.py::TestSelectNotes -v`
+Expected: PASS (3 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook_ask.py tests/test_notebook_ask.py
+git commit -m "feat(notebook): select_notes — structural retrieval for ask"
+```
+
+---
+
+### Task 2: Grounded synthesis — `answer_question` (forced tool)
+
+**Files:**
+- Modify: `gently/harness/memory/notebook_ask.py`
+- Test: `tests/test_notebook_ask.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_ask.py
+import asyncio
+
+from gently.harness.memory.notebook_ask import ASK_TOOL, answer_question, build_ask_messages
+
+
+class _FakeBlock:
+ def __init__(self, inp):
+ self.type = "tool_use"
+ self.input = inp
+
+
+class _FakeResp:
+ def __init__(self, inp):
+ self.content = [_FakeBlock(inp)]
+ self.stop_reason = "tool_use"
+
+
+class _FakeMessages:
+ def __init__(self, captured, inp):
+ self._captured, self._inp = captured, inp
+
+ async def create(self, **kwargs):
+ self._captured.update(kwargs)
+ return _FakeResp(self._inp)
+
+
+class _FakeClient:
+ def __init__(self, inp):
+ self.captured = {}
+ self.messages = _FakeMessages(self.captured, inp)
+
+
+class TestAnswerQuestion:
+ def test_build_messages_embeds_note_ids(self):
+ notes = [Note(id="o1", kind=NoteKind.OBSERVATION, body="ring formed")]
+ msgs = build_ask_messages("what formed?", notes)
+ text = msgs[0]["content"]
+ assert "o1" in text and "ring formed" in text and "what formed?" in text
+
+ def test_answer_returns_structured_and_forces_tool(self):
+ canned = {"answer": "A ring formed.", "points": [{"text": "ring formed", "note_ids": ["o1"]}],
+ "suggested_next": [], "coverage": "covered"}
+ client = _FakeClient(canned)
+ notes = [Note(id="o1", kind=NoteKind.OBSERVATION, body="ring formed")]
+ out = asyncio.run(answer_question(client, "m", "what formed?", notes))
+ assert out == canned
+ # tool_choice is pinned to the ask tool (forced structured output)
+ assert client.captured["tool_choice"] == {"type": "tool", "name": ASK_TOOL["name"]}
+ assert client.captured["model"] == "m"
+
+ def test_answer_no_notes_short_circuits_without_api(self):
+ client = _FakeClient({"should": "not be used"})
+ out = asyncio.run(answer_question(client, "m", "anything?", []))
+ assert out["coverage"] == "not_in_notebook"
+ assert client.captured == {} # no API call when nothing to ground on
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_ask.py::TestAnswerQuestion -v`
+Expected: FAIL — `ImportError: cannot import name 'ASK_TOOL'`
+
+- [ ] **Step 3: Write minimal implementation**
+
+Append to `gently/harness/memory/notebook_ask.py`:
+
+```python
+# ASK_TOOL pins the structured output. No confidence field — we don't ask the
+# model to self-rate (lab rule); "coverage" is a factual grounding classification.
+ASK_TOOL = {
+ "name": "answer_from_notebook",
+ "description": "Return a grounded answer built ONLY from the provided notebook entries.",
+ "input_schema": {
+ "type": "object",
+ "properties": {
+ "answer": {"type": "string", "description": "Direct synthesis grounded in the notes."},
+ "points": {
+ "type": "array",
+ "description": "Supporting points, each citing the note ids it rests on.",
+ "items": {
+ "type": "object",
+ "properties": {
+ "text": {"type": "string"},
+ "note_ids": {"type": "array", "items": {"type": "string"}},
+ },
+ "required": ["text", "note_ids"],
+ },
+ },
+ "suggested_next": {
+ "type": "array",
+ "items": {"type": "string"},
+ "description": "Concrete next experiments/moves if the question asks what to do; else empty.",
+ },
+ "coverage": {
+ "type": "string",
+ "enum": ["covered", "partial", "not_in_notebook"],
+ "description": "How well the provided notes cover the question.",
+ },
+ },
+ "required": ["answer", "points", "coverage"],
+ },
+}
+
+_SYSTEM = (
+ "You reason over a shared lab notebook. Answer ONLY from the notebook entries "
+ "provided — every claim must cite the note id(s) it rests on. If the notes do "
+ "not contain the answer, say so plainly and set coverage to 'not_in_notebook'. "
+ "Never invent facts not in the notes. Call the answer_from_notebook tool."
+)
+
+
+def _render_notes(notes: list[Note]) -> str:
+ lines = []
+ for n in notes:
+ scope = []
+ if n.strains:
+ scope.append("strains=" + ",".join(n.strains))
+ if n.embryos:
+ scope.append("embryos=" + ",".join(n.embryos))
+ tag = f" [{'; '.join(scope)}]" if scope else ""
+ lines.append(f"[{n.id}] ({n.kind.value}){tag} {n.body}")
+ return "\n".join(lines)
+
+
+def build_ask_messages(question: str, notes: list[Note]) -> list[dict]:
+ body = (
+ "Notebook entries:\n"
+ + _render_notes(notes)
+ + f"\n\nQuestion: {question}\n\n"
+ "Answer using only these entries, citing note ids."
+ )
+ return [{"role": "user", "content": body}]
+
+
+async def answer_question(client, model: str, question: str, notes: list[Note]) -> dict:
+ """Force the ask tool and return its validated input dict. Short-circuits
+ (no API call) when there are no notes to ground on."""
+ if not notes:
+ return {
+ "answer": "The notebook doesn't cover this yet.",
+ "points": [],
+ "suggested_next": [],
+ "coverage": "not_in_notebook",
+ }
+ resp = await client.messages.create(
+ model=model,
+ max_tokens=2048,
+ system=_SYSTEM,
+ tools=[ASK_TOOL],
+ tool_choice={"type": "tool", "name": ASK_TOOL["name"]},
+ messages=build_ask_messages(question, notes),
+ )
+ for block in resp.content:
+ if getattr(block, "type", None) == "tool_use":
+ return block.input
+ return {"answer": "", "points": [], "suggested_next": [], "coverage": "not_in_notebook"}
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_ask.py -q`
+Expected: all pass
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/harness/memory/notebook_ask.py tests/test_notebook_ask.py
+git commit -m "feat(notebook): answer_question — forced-tool grounded synthesis"
+```
+
+---
+
+### Task 3: `POST /api/notebook/ask`
+
+**Files:**
+- Modify: `gently/ui/web/routes/notebook.py`
+- Test: `tests/test_notebook_api.py` (append)
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# append to tests/test_notebook_api.py
+class _AskBlock:
+ def __init__(self, inp):
+ self.type = "tool_use"
+ self.input = inp
+
+
+class _AskResp:
+ def __init__(self, inp):
+ self.content = [_AskBlock(inp)]
+ self.stop_reason = "tool_use"
+
+
+class _AskMessages:
+ def __init__(self, inp):
+ self._inp = inp
+
+ async def create(self, **kwargs):
+ return _AskResp(self._inp)
+
+
+class _AskClient:
+ def __init__(self, inp):
+ self.messages = _AskMessages(inp)
+
+
+def _make_app_with_client(context_store, client):
+ from gently.ui.web.routes.notebook import create_router
+
+ app = FastAPI()
+
+ class _Server:
+ pass
+
+ server = _Server()
+ server.context_store = context_store
+ server.claude_async = client
+ app.include_router(create_router(server))
+ return app
+
+
+class TestAsk:
+ def test_ask_returns_grounded_answer(self, file_context_store):
+ cs = _seed(file_context_store)
+ canned = {"answer": "A ring formed.", "points": [{"text": "ring", "note_ids": ["o1"]}],
+ "suggested_next": [], "coverage": "covered"}
+ client = TestClient(_make_app_with_client(cs, _AskClient(canned)))
+ resp = client.post("/api/notebook/ask", json={"question": "what happened?"})
+ assert resp.status_code == 200
+ assert resp.json()["coverage"] == "covered"
+
+ def test_ask_no_store(self):
+ client = TestClient(_make_app(None))
+ resp = client.post("/api/notebook/ask", json={"question": "x"})
+ assert resp.json() == {"available": False}
+```
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_api.py::TestAsk -v`
+Expected: FAIL — 404/405 (route not defined)
+
+- [ ] **Step 3: Write minimal implementation**
+
+In `gently/ui/web/routes/notebook.py`, add `Body` to the fastapi import and add the route inside `create_router`, before `return router`:
+
+```python
+ @router.post("/api/notebook/ask")
+ async def ask(
+ question: str = Body(..., embed=True),
+ thread: str | None = Body(None, embed=True),
+ strain: str | None = Body(None, embed=True),
+ ):
+ nb = _nb()
+ if nb is None:
+ return {"available": False}
+ from gently.harness.memory.notebook_ask import answer_question, select_notes
+ from gently.settings import settings
+
+ notes = select_notes(nb, thread=thread, strain=strain)
+ client = getattr(server, "claude_async", None)
+ if client is None:
+ import anthropic
+
+ client = anthropic.AsyncAnthropic()
+ result = await answer_question(client, settings.models.main, question, notes)
+ result["available"] = True
+ result["note_ids"] = [n.id for n in notes]
+ return result
+```
+
+Update the import line at the top of the file:
+
+```python
+from fastapi import APIRouter, Body, HTTPException
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `.venv/bin/python -m pytest tests/test_notebook_api.py -q`
+Expected: all pass
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/routes/notebook.py tests/test_notebook_api.py
+git commit -m "feat(notebook): POST /api/notebook/ask — grounded notebook Q&A"
+```
+
+---
+
+## Self-Review
+**Spec coverage (design §4):** structural retrieval (`select_notes`) → grounded synthesis (`answer_question`, forced tool, cited, "not_in_notebook" valid) → endpoint. ✓ No self-rated confidence (`coverage` is grounding, not correctness-confidence). ✓ Reuses `settings.models.main`, `anthropic.AsyncAnthropic`, the `verifier.py` forced-tool pattern. ✓ Client injected → unit-testable without real API. ✓
+**Deferred (correct):** embeddings/semantic recall; the "Ask" UI; proactive surfacing.
+**Placeholder scan:** none — complete code + commands. ✓
+**Type consistency:** `select_notes`, `answer_question`, `ASK_TOOL`, `build_ask_messages` named consistently across tasks/tests; route uses `server.claude_async` (tests inject) with a real `AsyncAnthropic` fallback. ✓
diff --git a/docs/superpowers/plans/2026-06-27-temperature-interface.md b/docs/superpowers/plans/2026-06-27-temperature-interface.md
new file mode 100644
index 00000000..cfdaa563
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-27-temperature-interface.md
@@ -0,0 +1,793 @@
+# Temperature Interface Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Persist live temperature into each imaging session and chart it, so bursts/volumes are correlatable to temperature and the rise/fall trajectory is visible during the temperature-strain experiments.
+
+**Architecture:** A `FileStore`-backed append-only `temperature.jsonl` per session (mirroring `predictions.jsonl`); a `TemperatureSampler(Service)` in the agent process — modeled on `DeviceStateMonitor` — that, while a session is active, polls the device layer at 1 Hz, appends each reading, holds the latest in memory, and publishes a `TEMPERATURE_UPDATE` event that the viz server already forwards to the browser; acquisition code stamps the latest reading into volume/burst metadata; a FastAPI history route backfills the graph; a hand-rolled SVG component renders water-temp + stepped-setpoint as a card on the Devices tab.
+
+**Tech Stack:** Python 3 + asyncio, FastAPI, the project's `EventBus`, `FileStore` (file-based YAML/JSONL), vanilla-JS + hand-rolled SVG frontend (no build step), pytest with `asyncio_mode = "auto"`.
+
+## Global Constraints
+
+- **No new dependency** — chart is hand-rolled SVG (`createElementNS`), matching `experiment-overview.js`. No charting library.
+- **Session-scoped capture only** — sampler persists/emits **only while a session is active**; no always-on facility daemon.
+- **Sample schema** (one JSONL line): `{"t": No temperature data yet
';
+ }
+
+ function render() {
+ if (!_samples.length) { renderEmpty(); return; }
+ const W = _root.clientWidth || 480, H = 160, pad = 24;
+ const xs = _samples.map((_, i) => i);
+ const ws = _samples.map(s => s.water_c).filter(v => v != null);
+ const sps = _samples.map(s => s.setpoint_c).filter(v => v != null);
+ const lo = Math.min(...ws, ...sps) - 1, hi = Math.max(...ws, ...sps) + 1;
+ const sx = i => pad + (i / Math.max(1, xs.length - 1)) * (W - 2 * pad);
+ const sy = v => H - pad - ((v - lo) / Math.max(0.001, hi - lo)) * (H - 2 * pad);
+
+ const svg = document.createElementNS(SVGNS, "svg");
+ svg.setAttribute("viewBox", `0 0 ${W} ${H}`); svg.setAttribute("width", "100%");
+
+ const line = (pts, cls) => {
+ const p = document.createElementNS(SVGNS, "polyline");
+ p.setAttribute("points", pts); p.setAttribute("class", cls);
+ p.setAttribute("fill", "none"); svg.appendChild(p);
+ };
+ line(_samples.map((s, i) => s.water_c != null ? `${sx(i)},${sy(s.water_c)}` : "").filter(Boolean).join(" "), "temp-water");
+ // Stepped setpoint: carry previous y until it changes.
+ let sp = []; _samples.forEach((s, i) => { if (s.setpoint_c != null) sp.push(`${sx(i)},${sy(s.setpoint_c)}`); });
+ line(sp.join(" "), "temp-setpoint");
+
+ const last = _samples[_samples.length - 1];
+ const readout = document.createElement("div");
+ readout.className = "temp-graph-readout";
+ readout.textContent = `${last.water_c?.toFixed?.(1) ?? "—"} °C → ${last.setpoint_c?.toFixed?.(1) ?? "—"} °C (${last.state ?? ""})`;
+
+ _root.innerHTML = ""; _root.appendChild(readout); _root.appendChild(svg);
+ }
+
+ function dispose() { ClientEventBus.off("TEMPERATURE_UPDATE", onEvent); }
+ return { init, dispose, _render: render, _samples: () => _samples };
+})();
+window.TemperatureGraph = TemperatureGraph;
+```
+
+Add minimal CSS (in the devices stylesheet) for `.temp-water` (stroke: water color), `.temp-setpoint` (dashed stroke), `.temp-graph-empty` (muted), matching existing palette.
+
+- [ ] **Step 2: Mount it**
+
+In `templates/index.html`, add inside `#devices-content` (under the map/details view):
+
+```html
+
+```
+
+Load the script (next to the other `static/js/*.js` includes for the devices tab).
+
+In `devices.js` `init()` (`:1556`), after existing setup:
+
+```javascript
+ const tg = document.getElementById('devices-temp-graph');
+ if (tg && window.TemperatureGraph) TemperatureGraph.init(tg, 'current');
+```
+
+(No extra subscription needed in `devices.js` — the component self-subscribes. Optionally also route the existing small readout off the new event.)
+
+- [ ] **Step 3: Add the script tag**
+
+Add `` in `index.html` alongside the other component scripts, **before** `devices.js` loads (so `window.TemperatureGraph` exists when `init()` runs).
+
+- [ ] **Step 4: Verify live in the app (Chrome DevTools MCP)**
+
+Use the `run` skill to launch the app with the mock temperature backend and an active session. Then with Chrome DevTools MCP:
+- navigate to the Devices tab, take a snapshot/screenshot;
+- confirm the empty state shows when no samples, then the water trace + stepped setpoint render and update live as the sampler emits;
+- run the UI audit (alignment/spacing/overflow/contrast) per the "UI audit before done" practice and fix any flaws;
+- trigger one burst and confirm (Task 6) that `burst.yaml` + a frame `.meta.yaml` carry a `temperature` block.
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/static/js/temperature-graph.js gently/ui/web/templates/index.html gently/ui/web/static/js/devices.js
+git commit -m "feat(temperature): live SVG temperature graph on the Devices tab"
+```
+
+---
+
+## Self-Review
+
+**Spec coverage:**
+- Persistence / `temperature.jsonl` → Task 1. ✓
+- Sampler (poll @1 Hz, session-gated, latest-in-memory, SSE) → Tasks 2–4. ✓
+- Per-acquisition stamp (burst + volume) → Task 6. ✓
+- History API (`current` resolution, `since`) → Task 5. ✓
+- Reusable SVG graph on Devices card (water trace + stepped setpoint + readout, backfill + live, empty state) → Task 7. ✓
+- Error/empty handling (gap-not-crash, no-device idle, empty state) → Tasks 3 & 7. ✓
+- Out-of-scope (setpoint control, choreography, always-on) → not implemented, by design. ✓
+
+**Open verification items folded into tasks (not placeholders):** session-creation API (Task 1/3/6), `EventBus.subscribe` shape (Task 2), the agent's FileStore attribute (Task 4), the volume-meta accessor + `create_embryo` signature (Task 6), the burst-acquisition construction site (Task 6). Each is an explicit "confirm from the real API" instruction with a concrete fallback, not a TODO.
+
+**Type consistency:** `temperature_stamp` returns `{water_c, setpoint_c, state, sampled_at}` everywhere; sample lines are `{t, water_c, setpoint_c, state}`; event payload is `{session_id, sample}`; history response is `{session_id, samples}`. Consistent across Tasks 1/3/5/6/7.
diff --git a/docs/superpowers/plans/2026-06-28-manual-mode-live-view.md b/docs/superpowers/plans/2026-06-28-manual-mode-live-view.md
new file mode 100644
index 00000000..8c84eeeb
--- /dev/null
+++ b/docs/superpowers/plans/2026-06-28-manual-mode-live-view.md
@@ -0,0 +1,728 @@
+# Manual Mode — SPIM Live View (B1) Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** A Manual view in the Devices tab with a continuous SPIM single-slice brightfield live view (galvo/piezo/exposure controlled live), brightfield illumination, temperature, and by-hand burst/volume triggers — the hand-driven surface for next week's temperature-strain experiments.
+
+**Architecture:** A device-layer lightsheet live streamer (MMCore continuous sequence acquisition + peek-latest, parked galvo/piezo), bridged to the browser by a `LightSheetStreamMonitor` (EventBus `LIGHTSHEET_FRAME`) over the existing base64-JPEG/SSE→WS transport; `require_control` FastAPI proxy routes; a Manual view that mirrors the bottom-camera panel. FPS is measured on the rig; a binary transport path is a conditional follow-up.
+
+**Tech Stack:** Python asyncio + aiohttp (device layer), FastAPI (viz proxy), `pymmcore.CMMCore`, the project EventBus, vanilla-JS + canvas/SVG frontend (no build step), pytest (`asyncio_mode=auto`).
+
+## Global Constraints
+
+- **No new dependency.** Reuse `_encode_frame_for_stream` (OpenCV already present), the bottom-camera streamer pattern, A's portable temperature graph.
+- **Single SPIM camera** today: `self.devices.get("camera")` (`HamCam1`). No side-A/B selector in B1 (deferred to B2).
+- **Live = continuous sequence acquisition**, never a snap loop: `core.startContinuousSequenceAcquisition(0)` → peek `core.getLastImage()` (NEVER `popNextImage` — don't drain) → `stopSequenceAcquisition()` on exit. The core handle is `self.system.core` in the device layer; unwrap rpyc frames with `_safe_obtain`.
+- **Park** before/under live: `piezo.setPosition(z)`, `scanner.sa_offset_y.setPosition(deg)` (or `scanner.set_y_offset(deg)`), `scanner.set_spim_state("Idle")`, `piezo.set_spim_state("Idle")`. galvo/piezo updates apply live (no restart); exposure change → stop→`setExposure`→restart.
+- **Brightfield safety:** laser forced off in manual live (`setConfig(laser_group, "ALL OFF")` / `set_laser_power(...,0)`).
+- **Concurrency:** the streamer honors `self._state_pause_counter > 0` (heavy plan owns MMCore → back off / stop sequence). Only one live stream at a time.
+- **Lightsheet stream resolution/quality:** its own config — default `_ls_target_max_dim = 512`, `_ls_jpeg_quality = 70` (higher than the bottom-camera 360/55 thumbnail, for focus).
+- **Transport stays JSON/`send_text`** (no binary path exists); a binary hop is Task 8, conditional on the FPS measurement.
+- **Auth:** browser-facing writes are FastAPI proxy routes guarded by `Depends(require_control)`; device-layer aiohttp routes have no auth, so the browser must go through the proxy.
+- **Tests:** `pytest`; `file_store`-style fixtures; FastAPI `TestClient` + mock client for routes; a fake core for the streamer. Frontend has no JS unit harness → `node --check` + Chrome-MCP harness + UI audit. Much of B1 needs the real rig; off-rig we cover streamer (fake core), routes, client, frontend harness, and defer live/FPS verification.
+
+---
+
+### Task 1: `LIGHTSHEET_FRAME` event type + frontend exclusion
+
+**Files:**
+- Modify: `gently/core/event_bus.py` (enum near `:88`; `_NO_HISTORY_TYPES` near `:186-195`)
+- Modify: `gently/ui/web/static/js/websocket.js` (exclusion guard `:104-107`)
+- Test: `tests/test_lightsheet_event.py`
+
+**Interfaces:**
+- Produces: `EventType.LIGHTSHEET_FRAME` (declared with `auto()`, matching `BOTTOM_CAMERA_FRAME`), in `_NO_HISTORY_TYPES`. Wire serialization uses `.name` (so the browser receives `"LIGHTSHEET_FRAME"`).
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# tests/test_lightsheet_event.py
+from gently.core.event_bus import EventType, EventBus, _NO_HISTORY_TYPES
+
+def test_lightsheet_frame_event_exists():
+ assert EventType.LIGHTSHEET_FRAME.name == "LIGHTSHEET_FRAME"
+
+def test_lightsheet_frame_excluded_from_history():
+ assert EventType.LIGHTSHEET_FRAME in _NO_HISTORY_TYPES
+
+def test_lightsheet_frame_publishes_to_subscriber():
+ bus = EventBus()
+ seen = []
+ bus.subscribe(EventType.LIGHTSHEET_FRAME, lambda e: seen.append(e.data))
+ bus.publish(event_type=EventType.LIGHTSHEET_FRAME, data={"jpeg_b64": "x"}, source="t")
+ assert seen == [{"jpeg_b64": "x"}]
+```
+
+> Confirm `EventBus.subscribe` signature / `_NO_HISTORY_TYPES` exportability against the real file; adapt the import/subscribe if needed, keep the assertions.
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `pytest tests/test_lightsheet_event.py -v`
+Expected: FAIL — `AttributeError: LIGHTSHEET_FRAME`
+
+- [ ] **Step 3: Write minimal implementation**
+
+In `gently/core/event_bus.py`, add the member next to `BOTTOM_CAMERA_FRAME` (`:88`):
+```python
+ LIGHTSHEET_FRAME = auto() # Live JPEG frame from the SPIM lightsheet live stream
+```
+Add to `_NO_HISTORY_TYPES` (next to `BOTTOM_CAMERA_FRAME`):
+```python
+ EventType.LIGHTSHEET_FRAME, # high-volume live frames — keep out of history
+```
+In `gently/ui/web/static/js/websocket.js`, extend the exclusion guard (`:104-107`) so the frame skips the Events table but still reaches `ClientEventBus.emit`:
+```javascript
+ if (msg.event_type !== 'DEVICE_STATE_UPDATE' &&
+ msg.event_type !== 'BOTTOM_CAMERA_FRAME' &&
+ msg.event_type !== 'TEMPERATURE_UPDATE' &&
+ msg.event_type !== 'LIGHTSHEET_FRAME') {
+```
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `pytest tests/test_lightsheet_event.py -v` (3 passed) and `node --check gently/ui/web/static/js/websocket.js` (exit 0)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/core/event_bus.py gently/ui/web/static/js/websocket.js tests/test_lightsheet_event.py
+git commit -m "feat(manual-mode): add LIGHTSHEET_FRAME event type + frontend exclusion"
+```
+
+---
+
+### Task 2: Device-layer lightsheet live streamer (continuous sequence acquisition)
+
+**Files:**
+- Modify: `gently/hardware/dispim/device_layer.py` — add config attrs (near `:160-169`), `_park_lightsheet_sync`, `_grab_lightsheet_frame_sync`, `_lightsheet_streamer`, `_broadcast_lightsheet`, `handle_lightsheet_stream`, `handle_lightsheet_params`; register two routes (near `:2806`).
+- Test: `tests/test_lightsheet_streamer.py`
+- Reference (mirror): `_bottom_camera_streamer`/`_broadcast_camera`/`handle_bottom_camera_stream`/`_encode_frame_for_stream` (same file); sequence-acq calls in `devices/acquisition.py:186-254`.
+
+**Interfaces:**
+- Consumes: `self.system.core` (`pymmcore.CMMCore`), `self.devices["camera"]` / `["scanner"]` / `["piezo"]`, `self._state_pause_counter`, `self._encode_frame_for_stream` (reused verbatim), `_safe_obtain` (rpyc unwrap, imported as in `acquisition.py`).
+- Produces: SSE `GET /api/lightsheet/stream`; `POST /api/lightsheet/live/params` `{galvo, piezo, exposure}`; in-process live param state `self._ls_params`.
+
+> **Implementer confirmations (cannot be verified off-rig — confirm against the real code, do not guess silently):**
+> 1. The device-layer core handle (`self.system.core`) and that `pymmcore.CMMCore` exposes `startContinuousSequenceAcquisition(float)`, `getLastImage()`, `stopSequenceAcquisition()`, `isSequenceRunning()` (standard CMMCore API). If `getLastImage` is unavailable, use `getLastImageMD`/`getNBeforeLastImage`.
+> 2. The scanner/piezo park calls: `self.devices["scanner"].sa_offset_y.setPosition(deg)` and `self.devices["piezo"].setPosition(z)`, and `set_spim_state("Idle")` on both (from `devices/scanner.py:271`, `devices/piezo.py:226`, recon §3).
+> 3. Whether brightfield live needs the scanner/beam enabled or a static park — confirm against `../micro-manager/plugins/ASIdiSPIM/src/main/java/org/micromanager/asidispim/SetupPanel.java`. Default: static park, laser "ALL OFF".
+
+- [ ] **Step 1: Write the failing test (fake core + fake devices)**
+
+```python
+# tests/test_lightsheet_streamer.py
+import asyncio, numpy as np, pytest
+from gently.hardware.dispim.device_layer import DeviceLayer # confirm class name/import
+
+class FakeCore:
+ def __init__(self):
+ self.running = False; self.exposure = None; self.cam = None
+ self._frame = np.full((64, 64), 1000, dtype=np.uint16)
+ self.started = 0; self.stopped = 0
+ def setCameraDevice(self, n): self.cam = n
+ def getCameraDevice(self): return self.cam
+ def setExposure(self, n, ms): self.exposure = ms
+ def startContinuousSequenceAcquisition(self, interval): self.running = True; self.started += 1
+ def stopSequenceAcquisition(self): self.running = False; self.stopped += 1
+ def isSequenceRunning(self): return self.running
+ def getLastImage(self): return self._frame
+
+class FakeAxis:
+ def __init__(self): self.pos = None
+ def setPosition(self, v): self.pos = v
+
+class FakeScanner:
+ def __init__(self): self.sa_offset_y = FakeAxis(); self.name="Scanner"; self.state=None
+ def set_spim_state(self, s): self.state = s
+
+class FakePiezo(FakeAxis):
+ def __init__(self): super().__init__(); self.name="Piezo"; self.state=None
+ def set_spim_state(self, s): self.state = s
+
+def _streamer(dl):
+ dl.system = type("S", (), {"core": FakeCore()})()
+ dl.devices = {"camera": type("C", (), {"name": "HamCam1"})(),
+ "scanner": FakeScanner(), "piezo": FakePiezo()}
+ return dl
+
+async def test_grab_parks_and_peeks(monkeypatch):
+ dl = _streamer(DeviceLayer.__new__(DeviceLayer))
+ dl._state_pause_counter = 0
+ dl._ls_target_max_dim = 512; dl._ls_jpeg_quality = 70
+ dl._ls_params = {"galvo": 1.5, "piezo": 40.0, "exposure": 20.0}
+ dl._ls_seq_started = False; dl._ls_applied = {}
+ img = await asyncio.to_thread(dl._grab_lightsheet_frame_sync)
+ assert img is not None and img.shape == (64, 64)
+ assert dl.system.core.running is True # sequence started
+ assert dl.devices["piezo"].pos == 40.0 # piezo parked
+ assert dl.devices["scanner"].sa_offset_y.pos == 1.5 # galvo parked
+
+async def test_exposure_change_restarts_sequence():
+ dl = _streamer(DeviceLayer.__new__(DeviceLayer))
+ dl._state_pause_counter = 0
+ dl._ls_target_max_dim = 512; dl._ls_jpeg_quality = 70
+ dl._ls_params = {"galvo": 0.0, "piezo": 50.0, "exposure": 10.0}
+ dl._ls_seq_started = False; dl._ls_applied = {}
+ await asyncio.to_thread(dl._grab_lightsheet_frame_sync)
+ starts = dl.system.core.started
+ dl._ls_params["exposure"] = 30.0 # exposure change
+ await asyncio.to_thread(dl._grab_lightsheet_frame_sync)
+ assert dl.system.core.stopped >= 1 and dl.system.core.started == starts + 1
+ assert dl.system.core.exposure == 30.0
+```
+
+- [ ] **Step 2: Run to verify it fails**
+
+Run: `pytest tests/test_lightsheet_streamer.py -v`
+Expected: FAIL — `AttributeError: _grab_lightsheet_frame_sync` (or import).
+
+- [ ] **Step 3: Implement the streamer**
+
+Add config attrs in `__init__` (near `:169`, after the `_cam_*` block):
+```python
+ # Lightsheet (SPIM) live stream — continuous sequence acquisition.
+ self._ls_subscribers: list[asyncio.Queue] = []
+ self._ls_task: asyncio.Task | None = None
+ self._ls_interval_sec: float = 0.0 # peek as fast as exposure allows
+ self._ls_target_max_dim: int = 512
+ self._ls_jpeg_quality: int = 70
+ self._ls_params: dict = {"galvo": 0.0, "piezo": 50.0, "exposure": 20.0}
+ self._ls_seq_started: bool = False
+ self._ls_applied: dict = {} # last-applied galvo/piezo/exposure
+```
+
+Add the grab/park/peek (mirrors the sequence-acq calls from `acquisition.py`; uses `self.system.core`):
+```python
+ def _park_lightsheet_sync(self) -> None:
+ """Park scanner galvo + imaging piezo at the current live params (static sheet)."""
+ p = self._ls_params
+ scanner = self.devices.get("scanner")
+ piezo = self.devices.get("piezo")
+ if scanner is not None:
+ try: scanner.set_spim_state("Idle")
+ except Exception: pass
+ scanner.sa_offset_y.setPosition(float(p["galvo"]))
+ if piezo is not None:
+ try: piezo.set_spim_state("Idle")
+ except Exception: pass
+ piezo.setPosition(float(p["piezo"]))
+
+ def _ensure_lightsheet_sequence_sync(self) -> None:
+ """Start (or restart on exposure change) the continuous sequence on the SPIM camera."""
+ core = self.system.core
+ cam = self.devices.get("camera")
+ if cam is None:
+ raise RuntimeError("No lightsheet camera configured")
+ p = self._ls_params
+ need_restart = (
+ not self._ls_seq_started
+ or self._ls_applied.get("exposure") != p["exposure"]
+ )
+ if need_restart:
+ if core.isSequenceRunning():
+ core.stopSequenceAcquisition()
+ if core.getCameraDevice() != cam.name:
+ core.setCameraDevice(cam.name)
+ core.setExposure(cam.name, float(p["exposure"]))
+ core.startContinuousSequenceAcquisition(self._ls_interval_sec * 1000.0)
+ self._ls_seq_started = True
+ self._ls_applied["exposure"] = p["exposure"]
+
+ def _grab_lightsheet_frame_sync(self):
+ """Park → ensure sequence running → peek the latest frame (never drain)."""
+ try:
+ self._park_lightsheet_sync() # galvo/piezo applied live
+ self._ensure_lightsheet_sequence_sync() # start / restart on exposure
+ from gently.hardware.dispim.devices.acquisition import _safe_obtain
+ core = self.system.core
+ img = core.getLastImage()
+ try: img = _safe_obtain(img)
+ except (ImportError, AttributeError): pass
+ return np.asarray(img)
+ except Exception as exc:
+ logger.debug("Lightsheet grab failed: %s", exc)
+ return None
+
+ def _stop_lightsheet_sequence_sync(self) -> None:
+ try:
+ if self.system.core.isSequenceRunning():
+ self.system.core.stopSequenceAcquisition()
+ except Exception:
+ logger.debug("stop lightsheet sequence failed", exc_info=True)
+ self._ls_seq_started = False
+ self._ls_applied = {}
+```
+
+Add the streamer loop + broadcast (mirror `_bottom_camera_streamer`/`_broadcast_camera`, reusing `_encode_frame_for_stream`):
+```python
+ async def _lightsheet_streamer(self):
+ logger.info("Lightsheet streamer started")
+ try:
+ while self._ls_subscribers:
+ if self._state_pause_counter > 0:
+ # Heavy plan owns MMCore: release the sequence and back off.
+ if self._ls_seq_started:
+ await asyncio.to_thread(self._stop_lightsheet_sequence_sync)
+ await asyncio.sleep(0.1)
+ continue
+ tick = time.monotonic()
+ img = await asyncio.to_thread(self._grab_lightsheet_frame_sync)
+ payload = self._encode_frame_for_stream(img) if img is not None else None
+ if payload is not None:
+ await self._broadcast_lightsheet(payload)
+ elapsed = time.monotonic() - tick
+ # Pace to at least the exposure; peek-rate caps near the camera rate.
+ floor = max(self._ls_interval_sec, self._ls_params["exposure"] / 1000.0)
+ await asyncio.sleep(max(0.0, floor - elapsed))
+ except asyncio.CancelledError:
+ raise
+ except Exception:
+ logger.exception("Lightsheet streamer crashed")
+ finally:
+ await asyncio.to_thread(self._stop_lightsheet_sequence_sync)
+ logger.info("Lightsheet streamer exiting")
+
+ async def _broadcast_lightsheet(self, payload):
+ if not self._ls_subscribers:
+ return
+ dead = []
+ for q in self._ls_subscribers:
+ try:
+ q.put_nowait(payload)
+ except asyncio.QueueFull:
+ try:
+ _ = q.get_nowait(); q.put_nowait(payload)
+ except Exception:
+ dead.append(q)
+ for q in dead:
+ try: self._ls_subscribers.remove(q)
+ except ValueError: pass
+```
+
+> `_encode_frame_for_stream` uses `self._cam_target_max_dim`/`self._cam_jpeg_quality`. To get the 512/70 lightsheet settings without duplicating the encoder, add an optional override: change its signature to `_encode_frame_for_stream(self, img, max_dim=None, quality=None)` defaulting to the `_cam_*` values, and call it `self._encode_frame_for_stream(img, self._ls_target_max_dim, self._ls_jpeg_quality)` from the lightsheet loop. (One-line change to the encoder; bottom-camera behavior unchanged.)
+
+Add the SSE handler + params handler (mirror `handle_bottom_camera_stream`; the params handler updates `self._ls_params` — galvo/piezo apply on the next grab, exposure triggers the restart path):
+```python
+ async def handle_lightsheet_stream(self, request):
+ response = web.StreamResponse(status=200, reason="OK", headers={
+ "Content-Type": "text/event-stream", "Cache-Control": "no-cache",
+ "Connection": "keep-alive", "X-Accel-Buffering": "no"})
+ await response.prepare(request)
+ queue: asyncio.Queue = asyncio.Queue(maxsize=4)
+ self._ls_subscribers.append(queue)
+ if len(self._ls_subscribers) == 1 and (self._ls_task is None or self._ls_task.done()):
+ self._ls_task = asyncio.create_task(self._lightsheet_streamer(), name="lightsheet-streamer")
+ try:
+ await response.write(b": connected\n\n")
+ while True:
+ try:
+ payload = await asyncio.wait_for(queue.get(), timeout=10.0)
+ except asyncio.TimeoutError:
+ await response.write(b": keepalive\n\n"); continue
+ if payload is None: break
+ await response.write(f"data: {json.dumps(payload)}\n\n".encode())
+ except (asyncio.CancelledError, ConnectionResetError, ConnectionAbortedError):
+ pass
+ except Exception:
+ logger.exception("Lightsheet SSE writer failed")
+ finally:
+ try: self._ls_subscribers.remove(queue)
+ except ValueError: pass
+ return response
+
+ async def handle_lightsheet_params(self, request):
+ body = await request.json()
+ for k in ("galvo", "piezo", "exposure"):
+ if k in body and body[k] is not None:
+ self._ls_params[k] = float(body[k])
+ return web.json_response({"params": self._ls_params})
+```
+
+Register both routes near `:2806`:
+```python
+ self._app.router.add_get("/api/lightsheet/stream", self.handle_lightsheet_stream)
+ self._app.router.add_post("/api/lightsheet/live/params", self.handle_lightsheet_params)
+```
+
+- [ ] **Step 4: Run to verify it passes**
+
+Run: `pytest tests/test_lightsheet_streamer.py -v`
+Expected: PASS (2 passed). If `DeviceLayer.__new__` bypassing `__init__` leaves attrs unset, the test sets the needed ones explicitly (it does).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/hardware/dispim/device_layer.py tests/test_lightsheet_streamer.py
+git commit -m "feat(manual-mode): device-layer lightsheet live streamer (continuous sequence acquisition)"
+```
+
+---
+
+### Task 3: Client methods — `stream_lightsheet` + `set_lightsheet_live_params`
+
+**Files:**
+- Modify: `gently/hardware/dispim/client.py` (add two methods on `DiSPIMMicroscope`, near `stream_bottom_camera` `:913`)
+- Test: `tests/test_lightsheet_client.py`
+
+**Interfaces:**
+- Produces: `async def stream_lightsheet(self, timeout=None)` (async generator over `GET /api/lightsheet/stream`, identical SSE parse to `stream_bottom_camera`); `async def set_lightsheet_live_params(self, galvo=None, piezo=None, exposure=None) -> dict` (`POST /api/lightsheet/live/params`).
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# tests/test_lightsheet_client.py
+import pytest
+from gently.hardware.dispim.client import DiSPIMMicroscope
+
+async def test_set_params_posts_body(monkeypatch):
+ m = DiSPIMMicroscope.__new__(DiSPIMMicroscope)
+ sent = {}
+ async def fake_post(path, body):
+ sent["path"] = path; sent["body"] = body; return {"params": body}
+ m._api_post = fake_post # confirm the real low-level POST helper name
+ res = await m.set_lightsheet_live_params(galvo=1.0, piezo=42.0, exposure=15.0)
+ assert sent["path"] == "/api/lightsheet/live/params"
+ assert sent["body"] == {"galvo": 1.0, "piezo": 42.0, "exposure": 15.0}
+ assert res == {"params": {"galvo": 1.0, "piezo": 42.0, "exposure": 15.0}}
+
+def test_stream_lightsheet_is_async_generator():
+ m = DiSPIMMicroscope.__new__(DiSPIMMicroscope)
+ import inspect
+ assert inspect.isasyncgenfunction(m.stream_lightsheet)
+```
+
+> Confirm the real low-level POST helper (the recon shows `set_led` etc. POST via an internal helper — find whether it's `self._api_post(path, body)` or an inline `self._session.post`). Match it; if `set_lightsheet_live_params` should drop `None` keys, build the body from only the provided args (the test passes all three).
+
+- [ ] **Step 2: Run to verify it fails**
+
+Run: `pytest tests/test_lightsheet_client.py -v` → FAIL (no such methods).
+
+- [ ] **Step 3: Implement**
+
+```python
+ async def stream_lightsheet(self, timeout: float | None = None):
+ """Async generator yielding JPEG frames from the lightsheet live SSE stream.
+
+ Mirrors :meth:`stream_bottom_camera`; subscriber-gated on the device layer.
+ """
+ self._ensure_connected()
+ client_timeout = aiohttp.ClientTimeout(total=None, sock_read=timeout, sock_connect=10.0)
+ url = f"{self.http_url}/api/lightsheet/stream"
+ async with self._session.get(url, timeout=client_timeout) as resp:
+ resp.raise_for_status()
+ buf = b""
+ async for chunk in resp.content.iter_any():
+ if not chunk:
+ continue
+ buf += chunk
+ while b"\n\n" in buf:
+ event_block, buf = buf.split(b"\n\n", 1)
+ data_lines = []
+ for line in event_block.splitlines():
+ if not line or line.startswith(b":"):
+ continue
+ if line.startswith(b"data:"):
+ data_lines.append(line[5:].lstrip())
+ if not data_lines:
+ continue
+ raw = b"\n".join(data_lines).decode("utf-8", errors="replace")
+ try:
+ import json as _json
+ yield _json.loads(raw)
+ except Exception as exc:
+ logger.warning("Malformed lightsheet SSE payload skipped: %s", exc)
+
+ async def set_lightsheet_live_params(self, galvo=None, piezo=None, exposure=None) -> dict:
+ """POST live galvo/piezo/exposure to the device-layer lightsheet streamer."""
+ body = {}
+ if galvo is not None: body["galvo"] = float(galvo)
+ if piezo is not None: body["piezo"] = float(piezo)
+ if exposure is not None: body["exposure"] = float(exposure)
+ return await self._api_post("/api/lightsheet/live/params", body)
+```
+
+> If the real POST helper isn't `_api_post`, adapt this one call site (and the test) to the real helper. `stream_lightsheet` copies `stream_bottom_camera` verbatim except the URL.
+
+- [ ] **Step 4: Run to verify it passes**
+
+Run: `pytest tests/test_lightsheet_client.py -v` (2 passed)
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/hardware/dispim/client.py tests/test_lightsheet_client.py
+git commit -m "feat(manual-mode): client stream_lightsheet + set_lightsheet_live_params"
+```
+
+---
+
+### Task 4: `LightSheetStreamMonitor` (agent Service) + agent wiring
+
+**Files:**
+- Create: `gently/app/lightsheet_monitor.py`
+- Modify: `gently/app/agent.py` (init attr; construct in `start_viz_server` near the bottom-camera monitor `:849-860`; stop in `stop_viz_server` near `:862-869`)
+- Test: `tests/test_lightsheet_monitor.py`
+- Reference (mirror verbatim, swapping names/event): `gently/app/bottom_camera_monitor.py`
+
+**Interfaces:**
+- Consumes: `microscope.stream_lightsheet()` (Task 3); `EventType.LIGHTSHEET_FRAME` (Task 1).
+- Produces: `LightSheetStreamMonitor(Service)` with `running` property, `on_start`/`on_stop`; publishes `LIGHTSHEET_FRAME`. `agent.lightsheet_monitor` (constructed, not started; started via proxy in Task 5).
+
+- [ ] **Step 1: Write the failing test**
+
+```python
+# tests/test_lightsheet_monitor.py
+import asyncio
+from gently.core.event_bus import EventType, get_event_bus
+from gently.app.lightsheet_monitor import LightSheetStreamMonitor
+
+class FakeScope:
+ async def stream_lightsheet(self):
+ for i in range(3):
+ yield {"t": float(i), "jpeg_b64": f"f{i}"}
+ await asyncio.sleep(0)
+
+async def test_monitor_publishes_frames():
+ bus = get_event_bus()
+ seen = []
+ bus.subscribe(EventType.LIGHTSHEET_FRAME, lambda e: seen.append(e.data))
+ mon = LightSheetStreamMonitor(FakeScope(), reconnect_delay_sec=0.01)
+ await mon.start()
+ await asyncio.sleep(0.05)
+ await mon.stop()
+ assert any(d.get("jpeg_b64") == "f0" for d in seen)
+ assert mon.running is False
+```
+
+- [ ] **Step 2: Run to verify it fails** — `pytest tests/test_lightsheet_monitor.py -v` → FAIL (no module).
+
+- [ ] **Step 3: Implement** — copy `gently/app/bottom_camera_monitor.py` to `gently/app/lightsheet_monitor.py` and change: class → `LightSheetStreamMonitor`; `name="lightsheet-monitor"`; the `_run` loop calls `self.microscope.stream_lightsheet()` and publishes `EventType.LIGHTSHEET_FRAME` with `source="lightsheet-monitor"`. (Everything else — Service base, on_start/on_stop, reconnect loop, `_last_frame_ts`, `running` — is identical.)
+
+Agent wiring in `gently/app/agent.py`: add `self.lightsheet_monitor = None` next to `self.bottom_camera_monitor = None`; in `start_viz_server` (after the bottom-camera monitor construction `:849-860`):
+```python
+ if self.microscope is not None and self.lightsheet_monitor is None:
+ try:
+ from .lightsheet_monitor import LightSheetStreamMonitor
+ self.lightsheet_monitor = LightSheetStreamMonitor(self.microscope)
+ logger.info("Lightsheet monitor ready (not started)")
+ except Exception as e:
+ logger.warning(f"Failed to construct lightsheet monitor: {e}")
+ self.lightsheet_monitor = None
+```
+In `stop_viz_server` (near `:862`):
+```python
+ if self.lightsheet_monitor is not None:
+ try:
+ await self.lightsheet_monitor.stop()
+ except Exception:
+ logger.exception("Failed to stop lightsheet monitor")
+ self.lightsheet_monitor = None
+```
+
+- [ ] **Step 4: Run to verify it passes** — `pytest tests/test_lightsheet_monitor.py -v` (1 passed); `pytest -q` (no new failures).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/app/lightsheet_monitor.py gently/app/agent.py tests/test_lightsheet_monitor.py
+git commit -m "feat(manual-mode): LightSheetStreamMonitor bridge + agent wiring"
+```
+
+---
+
+### Task 5: Browser proxy routes (`require_control`)
+
+**Files:**
+- Modify: `gently/ui/web/routes/data.py` (add routes in `create_router`, mirroring the bottom-camera + room-light routes)
+- Test: `tests/test_lightsheet_routes.py`
+- Reference: bottom-camera start/stop/status (`data.py:260-311`), room-light proxy (`data.py:335-355`), `_resolve_client` (`:313`), `require_control` (`:10`).
+
+**Interfaces:**
+- Consumes: `agent.lightsheet_monitor` (Task 4); `client.set_lightsheet_live_params`, `set_led`, `set_laser_power`, `set_camera_led_mode`, `move_to_position`, `acquire_burst`, `acquire_volume`.
+- Produces (all `Depends(require_control)` except GET status): `POST /api/devices/lightsheet/live/{start,stop}`, `GET /api/devices/lightsheet/live/status`, `POST /api/devices/lightsheet/live/params`, `POST /api/devices/led/set`, `POST /api/devices/laser/off`, `POST /api/devices/camera/led_mode`, `POST /api/devices/stage/move`, `POST /api/devices/acquire/{burst,volume}`.
+
+- [ ] **Step 1: Write the failing test (TestClient + mock client/monitor)**
+
+```python
+# tests/test_lightsheet_routes.py
+from unittest.mock import MagicMock, AsyncMock
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+from gently.ui.web.routes.data import create_router
+import gently.ui.web.auth as auth
+
+def _app(client=None, monitor=None):
+ server = MagicMock()
+ server.agent_bridge.agent.client = client
+ server.agent_bridge.agent.lightsheet_monitor = monitor
+ app = FastAPI(); app.include_router(create_router(server))
+ # legacy localhost = CONTROL; TestClient client.host is "testclient" → force CONTROL:
+ app.dependency_overrides[auth.require_control] = lambda: True
+ return TestClient(app)
+
+def test_live_params_forwards():
+ client = MagicMock(); client.set_lightsheet_live_params = AsyncMock(return_value={"params": {}})
+ r = _app(client=client).post("/api/devices/lightsheet/live/params",
+ json={"galvo": 1.0, "piezo": 40.0, "exposure": 20.0})
+ assert r.status_code == 200
+ client.set_lightsheet_live_params.assert_awaited_once_with(galvo=1.0, piezo=40.0, exposure=20.0)
+
+def test_acquire_burst_forwards():
+ client = MagicMock(); client.acquire_burst = AsyncMock(return_value={"success": True, "request_id": "b1"})
+ r = _app(client=client).post("/api/devices/acquire/burst",
+ json={"frames": 60, "mode": "1hz", "num_slices": 1, "exposure_ms": 5.0})
+ assert r.status_code == 200 and r.json().get("request_id") == "b1"
+
+def test_live_start_requires_monitor():
+ r = _app(monitor=None).post("/api/devices/lightsheet/live/start")
+ assert r.status_code == 503
+```
+
+> Confirm the `require_control` override mechanism: in legacy mode `TestClient` requests are not localhost, so override the dependency (as above) to isolate route logic. A separate test can assert the gate by NOT overriding and expecting 403.
+
+- [ ] **Step 2: Run to verify it fails** — `pytest tests/test_lightsheet_routes.py -v` → FAIL (routes 404).
+
+- [ ] **Step 3: Implement** — in `create_router`, add (mirroring the referenced routes). Live start/stop/status copy the bottom-camera versions verbatim, swapping `bottom_camera_monitor` → `lightsheet_monitor`. Then:
+```python
+ @router.post("/api/devices/lightsheet/live/params", dependencies=[Depends(require_control)])
+ async def lightsheet_live_params(payload: dict = Body(...)): # noqa: B008
+ client = _resolve_client()
+ if client is None:
+ raise HTTPException(status_code=503, detail="Microscope not connected")
+ try:
+ res = await client.set_lightsheet_live_params(
+ galvo=payload.get("galvo"), piezo=payload.get("piezo"),
+ exposure=payload.get("exposure"))
+ except Exception as exc:
+ logger.exception("lightsheet live params failed")
+ raise HTTPException(status_code=502, detail=f"params failed: {exc}") from exc
+ return res
+
+ @router.post("/api/devices/led/set", dependencies=[Depends(require_control)])
+ async def led_set(payload: dict = Body(...)): # noqa: B008
+ client = _resolve_client()
+ if client is None: raise HTTPException(status_code=503, detail="Microscope not connected")
+ try: return await client.set_led(str(payload.get("state", "Closed")))
+ except Exception as exc:
+ raise HTTPException(status_code=502, detail=f"led failed: {exc}") from exc
+
+ @router.post("/api/devices/laser/off", dependencies=[Depends(require_control)])
+ async def laser_off():
+ client = _resolve_client()
+ if client is None: raise HTTPException(status_code=503, detail="Microscope not connected")
+ try: return await client.set_laser_power(488, 0) # confirm signature; force off
+ except Exception as exc:
+ raise HTTPException(status_code=502, detail=f"laser off failed: {exc}") from exc
+
+ @router.post("/api/devices/camera/led_mode", dependencies=[Depends(require_control)])
+ async def camera_led_mode(payload: dict = Body(...)): # noqa: B008
+ client = _resolve_client()
+ if client is None: raise HTTPException(status_code=503, detail="Microscope not connected")
+ try: return await client.set_camera_led_mode(bool(payload.get("use_led", False)))
+ except Exception as exc:
+ raise HTTPException(status_code=502, detail=f"camera led mode failed: {exc}") from exc
+
+ @router.post("/api/devices/stage/move", dependencies=[Depends(require_control)])
+ async def stage_move(payload: dict = Body(...)): # noqa: B008
+ client = _resolve_client()
+ if client is None: raise HTTPException(status_code=503, detail="Microscope not connected")
+ try: return await client.move_to_position(float(payload["x"]), float(payload["y"]))
+ except KeyError: raise HTTPException(status_code=400, detail="x and y required")
+ except Exception as exc:
+ raise HTTPException(status_code=502, detail=f"stage move failed: {exc}") from exc
+
+ @router.post("/api/devices/acquire/burst", dependencies=[Depends(require_control)])
+ async def acquire_burst(payload: dict = Body(...)): # noqa: B008
+ client = _resolve_client()
+ if client is None: raise HTTPException(status_code=503, detail="Microscope not connected")
+ try:
+ return await client.acquire_burst(
+ frames=int(payload.get("frames", 60)), mode=str(payload.get("mode", "1hz")),
+ num_slices=int(payload.get("num_slices", 1)),
+ exposure_ms=float(payload.get("exposure_ms", 5.0)))
+ except Exception as exc:
+ raise HTTPException(status_code=502, detail=f"burst failed: {exc}") from exc
+
+ @router.post("/api/devices/acquire/volume", dependencies=[Depends(require_control)])
+ async def acquire_volume(payload: dict = Body(...)): # noqa: B008
+ client = _resolve_client()
+ if client is None: raise HTTPException(status_code=503, detail="Microscope not connected")
+ try:
+ return await client.acquire_volume(
+ num_slices=int(payload.get("num_slices", 50)),
+ exposure_ms=float(payload.get("exposure_ms", 10.0)))
+ except Exception as exc:
+ raise HTTPException(status_code=502, detail=f"volume failed: {exc}") from exc
+```
+Add the live start/stop/status routes by copying `start_bottom_camera_stream`/`stop_bottom_camera_stream`/`get_bottom_camera_status` (`data.py:260-311`) under `/api/devices/lightsheet/live/...` with `getattr(agent, "lightsheet_monitor", None)`.
+
+- [ ] **Step 4: Run to verify it passes** — `pytest tests/test_lightsheet_routes.py -v` (3 passed); `pytest -q` (no new failures).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add gently/ui/web/routes/data.py tests/test_lightsheet_routes.py
+git commit -m "feat(manual-mode): require_control proxy routes for lightsheet live + illumination + acquire"
+```
+
+---
+
+### Task 6: Manual view (frontend)
+
+**Files:**
+- Modify: `gently/ui/web/templates/index.html` — add a `data-view="manual"` button to `#devices-view-switcher` (`:445-449`); add a `#devices-view-manual` container with the live canvas + control rail (model the camera panel `:569-608`); load A's `temperature-graph.js` if not already loaded on this page.
+- Modify: `gently/ui/web/static/js/devices.js` — add `'manual'` to `VIEWS` (`:17`); a `handleLightsheetFrame` (mirror `handleCameraFrame` `:1111`); live toggle (mirror `toggleCameraStream`); galvo/piezo/exposure controls (debounced POST `live/params`); illumination toggles; acquire buttons; FPS readout; embed `TemperatureGraph.init`; a `'v'`-style key handled only if not deferred — leave existing keys, add nothing conflicting.
+- Modify: the devices stylesheet for the manual panel (reuse `.devices-camera-*` styles where possible).
+
+**No JS unit harness** — verified by `node --check` + a Chrome-MCP harness (like A) + a UI audit.
+
+- [ ] **Step 1: Markup** — add to `#devices-view-switcher`:
+```html
+
+```
+Add the view container after `#devices-view-optical3d` (`:716`-ish), with: a live `