company-report

name

company-report

description

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".

Company Report

Produce a professional ~2-page corporate report on a publicly-traded company for a portfolio manager, delivered as a single self-contained report.html. The work runs as a five-stage pipeline of subagents that pass data through files in a per-run working directory.

Input

A company name or ticker (e.g. "Apple", "AAPL", "Coca Cola"). If the user did not name a company, ask for one before starting.

Setup

1. Pick a working ticker/slug for the run (use the obvious ticker if the user gave one; otherwise a short uppercase slug — the Data Retriever will confirm the real ticker).
2. Create the working directory output/<TICKER>/ (and output/<TICKER>/raw/).
3. This directory path is passed to every stage; agents read prior stages' outputs from it and write their own outputs into it.

Pipeline — run these subagents IN THIS ORDER

Dispatch each as a subagent (Agent tool) with the company name and the working directory path. Each stage depends on the previous one — run them sequentially, not in parallel. Stop and report to the user if a stage fails.

1. Generalist → writes 00_key_variables.md (key financial variables to focus on for this company).
2. Data Retriever → runs tools/edgar_fetch.py to fetch CIK + latest 10-K + companyfacts.json into raw/, and writes raw/meta.json. If the company can't be resolved, stop and ask the user to disambiguate (use the candidate list the tool prints).
3. Data Scientist → writes 02_financials.json (cleaned metrics, ratios, growth) from raw/companyfacts.json.
4. Financial Analyst → writes 03_analysis.md (rigorous analysis grounded in the cleaned data + 10-K).
5. Report Writer → writes report.html (the final ~2-page self-contained website).

Use the matching subagent type for each stage: generalist, data-retriever, data-scientist, financial-analyst, report-writer.

Output

The deliverable is output/<TICKER>/report.html. Report its path to the user and offer to open it in a browser (e.g. open output/<TICKER>/report.html on macOS).

Notes

• All data is grounded in the company's latest 10-K and XBRL facts from SEC EDGAR. There is no live market-price feed; valuation is framed qualitatively or via per-share metrics.
• The EDGAR tool uses only the Python standard library — no pip install needed. SEC requires a descriptive User-Agent; the default is set in the tool and can be overridden via the EDGAR_UA environment variable.
• Never fabricate financial figures. If data is missing, the report says so.