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.
npx skills add BruceLanLan/augur --skill augur-lynchgit clone --depth 1 https://github.com/BruceLanLan/augurWrote 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.
[](https://agentmods.dev/skills/brucelanlan/augur/augur-lynch)<a href="https://agentmods.dev/skills/brucelanlan/augur/augur-lynch"><img src="https://agentmods.dev/badge/skills/brucelanlan/augur/augur-lynch.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.04981 |
| Opus 5 | $0.00010 | $0.02491 |
| Sonnet 5 | $0.00004 | $0.00996 |
| Haiku 4.5 | $0.00002 | $0.00498 |
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 8d 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.
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。
目录
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）
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.
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.
- 8d ago First seen · 488 lines · 20 tokens per session scan A 022351403f2d
augur-lynch is a skill published in the GitHub repository BruceLanLan/augur (458 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 4,981 once invoked, about $0.0001 per session on Opus 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.
Other skills, from other repositories
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
correlation-analysis
Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
credit-analysis
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
etf-analysis
A framework for comparing exchange-traded funds (ETFs), which are funds bought and sold on a stock exchange and usually track an index, industry, asset, or strategy. It covers fees, how closely an ETF follows its target, trading activity, and portfolio use.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.