twCarllin

twCarllin / 10k-analysis

Public

Multi-agent pipeline for automated SEC 10-K / 10-Q investment research

34
2
100% credibility
Found Apr 29, 2026 at 34 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An AI-powered analyzer that processes public company SEC filings to produce detailed investment research reports comparing current and prior periods.

How It Works

1
📈 Discover the stock analyzer

You hear about a handy tool that turns complex company financial reports into easy-to-read investment insights.

2
🔧 Get it ready

You prepare the tool by connecting a smart AI service that helps it understand and summarize reports.

3
📊 Pick your company

Enter a stock symbol like HWM and the year, and choose if it's an annual or quarterly report.

4
🚀 Start the deep dive

Hit go, and the tool grabs the latest public company filing and breaks it into key parts like business, risks, and finances.

5
🤖 AI uncovers insights

Smart helpers review changes from last year, spot trends, bull and bear points, and flag any unusual details.

📄 Receive your report

Enjoy a polished PDF and summary with charts, key metrics, competitor views, and investment advice ready to guide your decisions.

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AI-Generated Review

What is 10k-analysis?

This Python tool runs a multi-agent AI pipeline to automate SEC 10-K and 10-Q investment research. Run `python main.py TICKER YEAR` (with flags like `--filing-type 10-Q --quarter Q1`), and it fetches filings, extracts XBRL metrics, analyzes sections like MD&A, risks, and footnotes using Claude or OpenAI models, then generates PDF reports with trend tables, quarterly charts, bull/bear cases, and prior-year comparisons. It turns raw 10k reports into actionable insights without manual reading.

Why is it gaining traction?

Its claude multi agent pipeline delivers structured 10k analysis ai—covering sentiment, financial quality flags, unusual operations, and supply chain risks—with auto-retries, dry-run testing, and checkpoint caching to handle long runs. Unlike basic scrapers, it outputs ready reports with XBRL trends and competitor mappings, making it a practical multi agent llm pipeline example for SEC data. Devs like the CLI simplicity for quick 10k report analysis prototypes.

Who should use this?

Quant devs automating stock screens via 10k sentiment analysis, fintech engineers prototyping multi agent systems for fundamental research, or solo investors building personalized 10k analysis workflows. Perfect for those grinding through filings manually or needing prior-period deltas in quarterly updates.

Verdict

Solid proof-of-concept for 10k analysis ability at low cost, but 1.0% credibility score and 34 stars signal early maturity—docs are CLI-focused, no tests visible. Try for personal pipelines; scale cautiously for teams.

(198 words)

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