bottom-up-analyst

Bottom-Up Analyst

An agent skill that turns one company into an earned investment thesis, written up as a detailed, auditable due-diligence memo. It is the analytical engine of a bottom-up research stack: given a ticker or a name, it drives SEC-filing and market-data tools for grounding, reasons over the evidence, classifies the business into an archetype, triangulates an intrinsic-value range, tries to kill its own thesis, and writes the memo.

Where it sits

The analyst in a three-layer stack: the data skills ground it, and pitch-like-lou renders a pitch from its memo. The analyst is the missing middle — it decides what to pull, reasons to a verdict, values the business, and writes the memo; the data skills decide nothing and Lou assumes the work is already done. See the stack overview for how the four compose. Production order: analyst → memo → (optionally) Lou pitches from it; the definition of done is a pitch-ready memo.

What it does

• Classifies the company into one of six archetypes and loads the matching playbook — compounder, hypergrowth, cyclical, turnaround/inflection, special-situation, deep-value — so the right questions get the weight. (Lou's three value-investing shapes are a subset; the rest extend past where he worked. "Anything else" falls back to the core method.)
• Normalizes GAAP into owner earnings — maintenance vs. growth capex, stock comp, deferred revenue, one-offs — and shows capital allocation as a year-by-year trend.
• Analyzes competitive position filings-first (including peers' filings for management commentary), using the web only for what filings genuinely can't give — and labels it.
• Values by triangulation, weighted by archetype, with a reverse-DCF ("what's priced in?") as a first-class lens alongside forward DCF, EPV, and multiples.
• Stress-tests every thesis against the archetype's disqualifiers and a borrowed discipline: separate what you know from what you believe, concede the weak points, never let conviction outrun the evidence.

Layout

• SKILL.md — the skill itself (the entry point an agent loads): the loop, archetype routing, how it drives the tools, and the valuation tooling.
• references/ — lazily-loaded guides: the memo template, normalization, competitive analysis, valuation, and one playbook per archetype (references/archetypes/).
• scripts/ — thin, self-documenting valuation tools:
  • dcf.py — two-stage DCF, forward (assumptions → intrinsic value) and reverse (price → implied growth), with a bear/base/bull sensitivity table.
  • epv.py — Earnings Power Value, the no-growth floor.

Setup

The valuation scripts are pure-Python (standard library only) — no install needed:

python scripts/dcf.py --help
python scripts/epv.py --help

For the data layer, install and configure sec-edgar-skill (it needs an EDGAR_IDENTITY) and, for market data, market-scout; this skill drives those tools but does not re-document them.

A note on scope

This skill produces analysis, not advice. It is a tool for doing research rigorously and honestly; it does not know your circumstances and nothing it writes is a recommendation to buy or sell a security. Its entire design — the honesty markup, the pre-mortem, the verified-vs-assumed tagging — exists to keep an LLM's fluent prose tethered to evidence, so that a human can audit every claim and reach their own judgment.