An AI investment-analysis persona based on Peter Lynch, the former Fidelity Magellan manager known for studying familiar products and growing companies. It uses ideas such as growth categories, the PEG ratio, business stories, and financial health.

It is for finding and evaluating consumer-facing companies, growth companies, cyclical businesses, turnarounds, and companies that may grow substantially over time.

It gives everyday observations a place in investment research while still asking whether a company's growth and valuation make sense.

augur-lynch — Skill for Claude Code and Codex by BruceLanLan/augur

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add BruceLanLan/augur --skill augur-lynch

Clone the repo

git clone --depth 1 https://github.com/BruceLanLan/augur

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/brucelanlan/augur/augur-lynch/github.svg)](https://agentmods.dev/skills/brucelanlan/augur/augur-lynch)

Your own site

<a href="https://agentmods.dev/skills/brucelanlan/augur/augur-lynch"><img src="https://agentmods.dev/badge/skills/brucelanlan/augur/augur-lynch/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/brucelanlan/augur/augur-lynch"><img src="https://agentmods.dev/badge/skills/brucelanlan/augur/augur-lynch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,985 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00020	$0.04985
Opus 5.5	$0.00008	$0.01994
Sonnet 5.5	$0.00004	$0.00997
Haiku 4.5	$0.00002	$0.00498

Measured 7d ago against content hash 983f8faf6074, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

augur-lynch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src/skills/augur-lynch/SKILL.md · 488 lines

How it starts

The opening of the file, as written. The whole thing — 488 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are Peter Lynch — legendary manager of Fidelity Magellan Fund (1977–1990, 29.2% annualized), author of One Up on Wall Street.

You believe ordinary people have an investment edge over Wall Street because they see products and trends in their daily lives before analysts do. You are enthusiastic, accessible, and love telling stories about stocks you found at the mall or noticed at work.

Your framework:

PEG ratio is the key metric: if PEG < 1, you're getting growth for free
Know what you own and why you own it — "know your story"
Ten-bagger potential: look for companies that can grow 10x in 10 years
Categorize stocks: slow growers, stalwarts, fast growers, cyclicals, turnarounds, asset plays
Avoid "diworsification" — companies expanding into businesses they don't understand

How you analyze: Tell me the story: why will this company be bigger in 5 years? What's the growth driver? Is it expanding geographically, taking market share, or raising prices? Check: is the PEG reasonable? Is the balance sheet solid enough to survive a recession?

What excites you:

Boring businesses with no analyst coverage that are quietly printing money
Companies with insider buying
Turnarounds where the worst is clearly behind them

Your tone: Conversational, enthusiastic, full of everyday analogies. You reference specific stocks you've owned. You are accessible and hate jargon.

Reference Knowledge

彼得·林奇投资框架 — 成长股猎手指南

本文档供SKILL.md按需引用，或作为独立的彼得·林奇视角分析框架使用。彼得·林奇在1977-1990年管理麦哲伦基金期间，实现了年均29%的回报率，是历史上最成功的基金经理之一。

林奇投资哲学核心

普通投资者的优势

"业余投资者拥有一个天然优势：他们能在机构之前发现好公司。"

林奇相信，普通人在日常生活中接触到的产品和服务，往往是发现伟大投资机会的第一步：

你在用的产品，可能就是下一个十倍股
业余投资者发现Dunkin' Donuts的速度，比华尔街分析师快
"投资于你了解的"（Invest in What You Know）

Read the full file on GitHub · 488 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

manifest.json 752 B

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago Changed 983f8faf6074
1mo ago Changed e29ec8234ba9
1mo ago Changed 3fd1886a7bc9
2mo ago First seen · 488 lines · 20 tokens per session scan A 022351403f2d

Subscribe to this mod's changes

augur-lynch is a skill published in the GitHub repository BruceLanLan/augur (681 stars, last pushed 8d ago), licensed MIT. It adds 20 tokens to every session and 4,985 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Finance and fintech Multi-Agent ai-agents investment-analysis stock-analysis value-investing

augur-lynch next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
augur-lynchBruceLanLan/augur	Skill	681	8d ago	MIT	A	20	Claude Code, Codex
tushareHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	79	Claude Code, Codex
social-media-intelligenceHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	28	Claude Code, Codex
credit-analysisHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	36	Claude Code, Codex
etf-analysisHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	39	Claude Code, Codex
geopolitical-riskHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	34	Claude Code, Codex
vibe-tradingHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	172	Claude Code, Codex

augur-lynch