القائمة
Explain a credit decision for an applicant and draft a grounded adverse-action notice. Usage /explain <applicant>
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
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 | explain |
| description | Explain a credit decision for an applicant and draft a grounded adverse-action notice. Usage /explain <applicant> |
Run the attribute → check → write loop and save a report.
python -m tools.attribute "<applicant>" > reports/_attribution.json
Read it; note the decision, probability, and the most negative features.python -m tools.check --attribution reports/_attribution.json
If ok is false, stop and re-run step 1 — a non-additive attribution is not faithful
and may not be used.decision == "approve", write a short approval note; no adverse-action reasons.decision == "deny", write an adverse-action notice (adverse-action-writer
agent): the principal reasons are the features with the most negative phi, at most
four, each cited by its adverse_action_reason. Never cite a protected attribute.reports/<applicant>.md:
phi,Every factor in the notice must come from reports/_attribution.json. No outside reasons,
no protected attributes, no numbers the tool did not produce.
Try these to start:
/explain alice ← denied; recent delinquencies dominate./explain carol ← denied; high credit utilization dominates./explain bob ← approved; no adverse-action reasons.