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Answer a question about the bundled NovaCorp filings, grounded and graded. Usage /ask "<question>"
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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>"
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.