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Run a finance text task through the general and finance-domain classifiers, score both against gold labels, and report which wins and why. Usage /compare
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Based on SOC occupation classification
Value a public company's equity end-to-end (FCFE at CAPM cost of equity) with an IB deal team — multi-year EDGAR data, scenario forecasting, Monte-Carlo DCF, dual-source comps, a verification gate, and an investment-committee sign-off. Run `/valuation <cik|ticker>`.
Value a public company end-to-end from its CIK or ticker. Run `/valuation <cik|ticker>`. Orchestrates EDGAR fetch, three parallel valuation lanes (Monte-Carlo DCF, dual-source comparables, qualitative/risk), and reconciliation into a fair-value median + P10–P90, benchmarked against the real market price.
Generate a professional 2-page report on a publicly-traded company as a self-contained HTML website. Given a company name, orchestrates five subagents in order (Generalist, Data Retriever, Data Scientist, Financial Analyst, Report Writer) — pulling the latest 10-K from SEC EDGAR, cleaning the financials, analyzing them, and writing the report. Use when the user asks for a company report, equity write-up, or 10-K-based analysis. Triggers include "write a report on <company>", "company report for <X>", "/company-report".
One sentence on what this command does and when to invoke it. Usage /my-skill "<arg>"
Answer a question about the bundled NovaCorp filings, grounded and graded. Usage /ask "<question>"
Route a research question, gather grounded evidence, and write a cited brief. Usage /brief "<question>"
| name | compare |
| description | Run a finance text task through the general and finance-domain classifiers, score both against gold labels, and report which wins and why. Usage /compare |
Run the general-vs-domain comparison and save a grounded report.
python -m tools.general
Record the general-purpose accuracy and the sentences it missed.python -m tools.domain
Record the finance-domain accuracy and the sentences it flipped.python -m tools.compare --json
This is the source of truth: general_accuracy, domain_accuracy, winner,
margin, and domain_wins (each with its deciding_terms).domain_wins — e.g. the general lexicon scoring
beat as negative, never seeing headwinds/impairment, and misreading liability
as negative. Every number and term must come from step 3's output; cite nothing else.reports/compare.md:
Run it on a single sentence instead:
python -m tools.general "Revenue beat guidance and margins expanded."python -m tools.domain "Revenue beat guidance and margins expanded."
The two labels disagree on beat — the smallest version of the whole point.