An Agent Skills open-standard skill for Peter Lynch-style public company research. Give it a ticker or company name, choose a quick screen or a deep dive, and it guides a compatible AI agent through current web research, source verification, and a cited markdown report.

This repository is a skill package, not a web application. It has no Django server, database, frontend build, bundled data fetcher or project-specific API keys. The skill relies on the host agent's web search and page-fetch tools.

About Peter Lynch

Peter Lynch ran the Fidelity Magellan Fund from 1977 to 1990, delivering an average annual return of 29.2%, beating the S&P 500 index — making it one of the best-performing mutual funds in history.

His investment philosophy is grounded in a simple but powerful idea: ordinary investors have a real edge when they pay attention to the world around them.

• Invest in what you understand — focus on businesses whose products, services, and revenue model you can clearly explain.
• Focus on fundamentals — prioritize earnings growth, financial health, and business quality over short-term market movements or predictions.
• Growth at a reasonable price (GARP) — seek companies that combine solid growth with sensible valuation.
• Think long-term — durable business performance matters far more than short-term market noise.
• Use your individual advantage — everyday observations can help you spot great investments before Wall Street does.

"The person that turns over the most rocks wins the game." — Peter Lynch

He is best known for the books: Learn to Earn, One Up on Wall Street, and Beating the Street.

I created this project to see whether AI agents could generate stock research reports inspired by Peter Lynch's investing philosophy. After a few months of analyzing his books and iterating on the methodology/prompt engineering, it started producing surprisingly useful reports.

What It Does

The skill supports two research workflows:

Both workflows are designed around evidence discipline:

• Use the smallest reliable source set that can support the conclusion.
• Date time-sensitive market data.
• Cite every web-derived factual claim with a linked source: line.
• Do not invent missing financial data.
• Do not expose research scratchwork in the final report unless the user asks for it.

Quick Research

Quick Research is a triage workflow. It answers one practical question:

Is this company worth spending 2-5 hours researching in detail?

The quick workflow intentionally avoids annual reports, full filings, SEC detail pages, company quarterly reports, and long investor-relations documents. It uses reputable aggregator pages for identity, business description, financial trends, cash flow, balance sheet risk, valuation, and recent trend checks.

Default evidence budget:

• 1-3 aggregator sources
• 2-3 searches
• 2-3 page fetches

The report follows the quick methodology in references/quick/methodology.md and ends with one of these decisions:

• SKIP
• WATCHLIST
• DEEP RESEARCH

Deep Research

Deep Research is a full due-diligence workflow. It starts with official sources and treats the latest annual report, 10-K, 20-F, or equivalent full-year filing as the factual base whenever available.

The deep workflow then fills methodology gaps with targeted external sources for items such as live valuation, ownership, insider activity, peer context, industry structure, recession resilience, recent updates, and material legal or regulatory risks.

Default evidence budget:

• 7-10 sources
• 8-12 searches
• 10-16 page or document fetches

The report follows the deep methodology in references/deep/methodology.md and ends with an attractiveness score:

Attractiveness score: X / 10

How To Use

Install the folder in any AI tool that supports the Agent Skills format, then ask for research by ticker or company name.

Example prompts:

Do a quick screen of ASML.AS

Do a deep research on IBM company

Full analysis of Nintendo, Japan listing

If the request is ambiguous, the skill asks whether you want quick screening or deep due diligence. If only a brand, ambiguous company name, or ambiguous ticker is provided, the skill resolves the traded entity before analysis.

Useful optional hints:

• ticker
• exchange
• country
• primary listing
• company name if the ticker is ambiguous

Do not provide API keys or annual report PDFs. The skill is designed to find the needed public sources through the host agent's browsing tools.

Installation

Installation is client-specific. Agent Skills are supported by multiple AI tools and agentic clients; use the official Client Showcase to find instructions for your tool.

Repository Structure

.
|-- SKILL.md
|-- references
|   |-- disclaimer.md
|   |-- filing-rules.md
|   |-- quick
|   |   |-- methodology.md
|   |   |-- system-prompt.md
|   |   `-- web-research.md
|   `-- deep
|       |-- methodology.md
|       |-- system-prompt.md
|       `-- web-research.md
|-- LICENSE
`-- README.md

How The Files Work Together

The skill is intentionally split into a small entrypoint and focused reference files. A compatible agent first sees only the metadata in SKILL.md. When a stock-research request matches the description, the agent reads the full skill instructions, chooses Quick or Deep Research, then loads only the workflow-specific reference files it needs.

The normal flow is:

1. SKILL.md routes the request and sets the operating rules.
2. A workflow-specific web-research.md file tells the agent what sources to gather and when to stop.
3. A workflow-specific methodology.md file defines the analytical checklist.
4. A workflow-specific system-prompt.md file constrains the final report format, citations, and language.
5. references/disclaimer.md is appended after the report body.

File Guide

Customizing The Skill

For most changes, edit the smallest file that owns the behavior:

• Triggering or routing: SKILL.md
• Research source selection: the relevant web-research.md
• Investment logic: the relevant methodology.md
• Final report style: the relevant system-prompt.md
• Legal language: references/disclaimer.md

That separation keeps the skill easier to tune. You can make Quick Research stricter without touching Deep Research, or change Deep report formatting without changing the source-gathering rules.

Methodology

The research framework is inspired by the investment principles popularized in Peter Lynch's books, especially the emphasis on understandable businesses, earnings growth, financial strength, valuation discipline, and knowing what kind of business you are analyzing.

The skill does not force every company into a single label. Instead, it builds a diagnostic profile across traits such as:

• cyclicality
• turnaround status
• growth profile
• business maturity
• asset angle
• dominant analytical lenses

That profile determines which checks matter most. A cyclical company, a turnaround, a fast grower, a stalwart-like compounder, and an asset play should not be judged by the same shortcut.

Citation Standard

Final reports use claim-level citations. Source lines are placed directly below the claim they support:

- Revenue grew from $10.0 billion in FY2022 to $12.0 billion in FY2024.
  source: [Annual Report (FY2024)](https://example.com/annual-report)

The skill should not cite a source that is absent from the internal Source Ledger, and it should not include a factual claim unless it can trace that claim to a linked source.

Disclaimer

This project is independent and is not affiliated with, endorsed by, sponsored by, or associated with Peter Lynch, Fidelity Investments, or any related organization.

Reports generated with this skill are AI-generated and may contain errors or inaccuracies. They are provided for educational and informational purposes only and do not constitute financial, investment, tax, accounting, or legal advice. Always verify information independently before making investment decisions.

The skill tends to perform poorly on companies that have recently gone public.

License

Distributed under the MIT License — see LICENSE for details.

_{Created by Szymon Nycz · Inspired by Peter Lynch's philosophy}