القائمة
Score the bundled headlines, aggregate them into a daily signal, and write a grounded summary. Usage /sentiment [YYYY-MM-DD]
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 | sentiment |
| description | Score the bundled headlines, aggregate them into a daily signal, and write a grounded summary. Usage /sentiment [YYYY-MM-DD] |
Run the score -> aggregate -> summarize pipeline and save a report. With no date, process every bundled day; with a date, process just that one.
python -m tools.aggregate > reports/_signal.json
# or for one day:
python -m tools.aggregate --date 2024-02-13 > reports/_signal.json
reports/_signal.json: each date has a mean, a label, a count, and the
item_ids that fed it; headlines holds each scored item.python -m tools.lexicon "<headline>" to see which words and modifiers moved it.mean and label verbatim and cites the headlines that drove it by id. Never state
a polarity or mean that is not in reports/_signal.json.reports/<date>.md (or reports/signal.md for all dates):
Try these to start:
/sentiment — all three days; expect a bullish, a bearish and a neutral day./sentiment 2024-02-13 — the bearish day; see which headline weighed most./sentiment 2024-02-14 — a near-zero day where one negated headline ("does not beat")
cancels a positive one; a good test of whether the summary respects the tool's signs.