القائمة
Score a bundled model's answers against the gold finance QA set and report honest metrics + calibration. Usage /evaluate [predictions.json]
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | evaluate |
| description | Score a bundled model's answers against the gold finance QA set and report honest metrics + calibration. Usage /evaluate [predictions.json] |
Run the full evaluation and write a grounded report. Default predictions file:
data/candidates_overconfident.json. The tools own every number; you only run them,
read them back, and explain them.
Score (scorer agent):
python -m tools.score --gold data/gold.json --pred <predictions.json> --out reports/_scored.json
Record accuracy, mean_f1, n, n_correct.
Calibrate (calibration-analyst agent):
python -m tools.calibration --scored reports/_scored.json
Record ece, mean_confidence, the confident_wrong list, and the reliability bins.
Report (reviewer agent): write reports/<model>.md with, every figure cited from
the tools above:
Rules: report the numbers honestly; never round away a failure. Do not call an
overconfident model "well calibrated" because its accuracy looks acceptable. If the
confident_wrong list is non-empty, it leads the report.
Try these to start:
/evaluate → evaluates data/candidates_overconfident.json; expect ~0.5 accuracy, high ECE, five confident-wrong cases./evaluate data/candidates_calibrated.json → a model whose confidence tracks its accuracy; expect low ECE and no confident-wrong cases.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>"