Merge pull request #323 from Integration-Automation/dev

Release: merge dev into main (feature docs v4–v114, 130+ headless utils)

Author: JE-Chen

.gitignore

@@ -13,6 +13,7 @@ __pycache__/
 build/
 develop-eggs/
 dist/
+node_modules/
 downloads/
 eggs/
 .eggs/

CLAUDE.md

@@ -229,7 +229,7 @@ Run before every commit; fix all new findings:
 pip install ruff pylint bandit radon
 ruff check je_auto_control/
 pylint je_auto_control/
-bandit -r je_auto_control/ -x je_auto_control/test
+bandit -c pyproject.toml -r je_auto_control/   # uses [tool.bandit] excludes/skips
 radon cc je_auto_control/ -a -nc   # flags functions with CC >= C (>10)
 ```
 

README.md

@@ -97,7 +97,7 @@
   "devDependencies": {
     "@types/node": "^20.0.0",
     "@types/vscode": "^1.85.0",
-    "esbuild": "^0.25.0",
+    "esbuild": "^0.28.1",
     "typescript": "^5.4.0",
     "vscode-languageclient": "^9.0.1"
   }

README/README_zh-CN.md

@@ -9,8 +9,14 @@ qt-material==2.17
 mss==10.2.0
 defusedxml==0.7.1
 
+# Office I/O ([office] extra) — exercised by the headless Office tests.
+openpyxl==3.1.5
+python-docx==1.2.0
+python-pptx==1.0.2
+
 # Quality tooling — used by .github/workflows/quality.yml and locally.
 ruff==0.15.14
 bandit==1.9.4
 pytest==9.0.3
 pytest-timeout==2.4.0
+pytest-rerunfailures==15.1

README/README_zh-TW.md

@@ -0,0 +1,46 @@
+Near-Duplicate Text Detection (SimHash / MinHash)
+=================================================
+
+``fuzzy.fuzzy_dedupe`` is O(n²) pairwise ``SequenceMatcher`` with no stable
+fingerprint, and ``image_dedup`` only hashes pixels. This adds text
+fingerprints — SimHash (Hamming-distance near-dup) and MinHash (estimated
+Jaccard) — that scale and give a reusable signature, the text analog of the
+perceptual image hash.
+
+Pure standard library (``hashlib`` / ``re``); imports no ``PySide6``. A fixed
+hash (``blake2b``, not the salted built-in ``hash()``) keeps fingerprints
+deterministic across runs and CI.
+
+Headless API
+------------
+
+.. code-block:: python
+
+    from je_auto_control import (
+        simhash, near_duplicates, minhash_signature, minhash_similarity,
+    )
+
+    h1 = simhash("the quick brown fox jumps over the lazy dog")
+    h2 = simhash("the quick brown fox jumps over the lazy dogs")
+    # small Hamming distance ⇒ near-duplicate
+
+    clusters = near_duplicates(docs, max_distance=12)   # groups of indices
+
+    sig_a = minhash_signature(text_a)
+    minhash_similarity(sig_a, minhash_signature(text_b))  # ~ Jaccard
+
+``simhash`` returns a ``bits``-wide fingerprint from word shingles;
+``hamming_distance`` (shared with ``image_dedup``) measures bit difference.
+``near_duplicates`` clusters texts whose SimHashes are within ``max_distance``
+bits, returning a partition of indices (singletons included).
+``minhash_signature`` / ``minhash_similarity`` give a MinHash signature and a
+Jaccard estimate for set-overlap style dedup. Run ``normalize_text`` first for
+accent/form-insensitive fingerprints.
+
+Executor commands
+-----------------
+
+``AC_simhash`` returns ``{simhash}`` for a ``text``; ``AC_near_duplicates``
+returns ``{clusters}`` for ``texts`` within ``max_distance``. Both are exposed
+as MCP tools (``ac_simhash`` / ``ac_near_duplicates``) and as Script Builder
+commands under **Data**.

autocontrol-lsp/vscode/package-lock.json

@@ -0,0 +1,41 @@
+Single-Series Anomaly Detection
+===============================
+
+``data_drift`` answers "did the *distribution* shift between two batches" — it
+cannot point at *which* value in one live series is anomalous — and
+``slo.burn_alerts`` only thresholds error-budget burn, not arbitrary metric
+values (latency spikes, cost spikes, CPU). This flags outliers in a single
+series via z-score, robust MAD (modified z-score), and an EWMA control chart.
+
+Pure standard library (``math`` / ``statistics``); imports no ``PySide6``. Every
+function is pure (values in, flags out), so it is fully deterministic in CI.
+
+Headless API
+------------
+
+.. code-block:: python
+
+    from je_auto_control import detect_anomalies, mad_anomalies, ewma_control
+
+    series = [10, 11, 9, 10, 12, 10, 95, 11, 10]    # index 6 is the spike
+    mad_anomalies(series)                            # [6]  (robust)
+    detect_anomalies(series, method="mad")
+    # [{index, value, score, is_anomaly}, ...]
+
+    ewma_control(values, alpha=0.5, target_mean=10, target_sigma=1)  # shift indices
+
+``detect_anomalies`` scores each value (``mad`` default, or ``zscore``) and flags
+those past the threshold (3.5 for MAD, 3.0 for z-score). ``mad_anomalies`` /
+``zscore_anomalies`` return just the flagged indices, and ``mad_scores`` /
+``zscore_scores`` the raw scores. MAD (Iglewicz-Hoaglin modified z-score) is
+robust to outliers inflating the spread, so it stays sensitive where a plain
+z-score would not. ``ewma_control`` is an EWMA control chart for sustained
+level shifts — pass ``target_mean`` / ``target_sigma`` for an in-control
+baseline (else the series' own stats).
+
+Executor command
+----------------
+
+``AC_detect_anomalies`` takes a ``values`` list (optional ``method`` /
+``threshold``) and returns ``{results}``. It is exposed as the MCP tool
+``ac_detect_anomalies`` and as a Script Builder command under **Data**.