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Answer a question about the bundled NovaCorp filings, grounded and graded. Usage /ask "<question>"
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
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>"
Route a research question, gather grounded evidence, and write a cited brief. Usage /brief "<question>"
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
| name | ask |
| description | Answer a question about the bundled NovaCorp filings, grounded and graded. Usage /ask "<question>" |
Run the full Perceive → Reason → Act → Check loop and save a report.
python -m tools.retrieve "<question>" -k 4 > reports/_context.jsonreports/_context.json,
citing chunk ids for every figure.python -m tools.grade --question "<question>" --answer "<answer>" --context reports/_context.jsonfaithfulness < 0.7 revise the answer (or declare it unanswerable); if
relevance < 0.5 re-retrieve with a better query. Loop at most 3 times.reports/<slug>.md:
Try these to start:
/ask "What is NovaCorp's customer concentration risk?"/ask "How did gross margin change year over year, and why?"/ask "What was NovaCorp's net income?" ← not in the corpus; the agent must say so.