AlphaGBM Skills is a collection of AI-agent skills for researching options and other financial markets with sourced market data, including measures such as implied volatility, Greeks, skew, and trading flow. Traders use it with coding agents such as Claude Code, Cursor, and Windsurf to analyze instruments and research ideas using the project's data and scoring methods. The catalogue entries are the skills that bring these capabilities into agent workflows.
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 AlphaGBM/skills --skill alphagbm-buffett-analysisgit clone --depth 1 https://github.com/AlphaGBM/skillsWrote 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/alphagbm/skills/alphagbm-buffett-analysis)<a href="https://agentmods.dev/skills/alphagbm/skills/alphagbm-buffett-analysis"><img src="https://agentmods.dev/badge/skills/alphagbm/skills/alphagbm-buffett-analysis/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.
<a href="https://agentmods.dev/skills/alphagbm/skills/alphagbm-buffett-analysis"><img src="https://agentmods.dev/badge/skills/alphagbm/skills/alphagbm-buffett-analysis.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00180 | $0.01724 |
| Opus 5 | $0.00090 | $0.00862 |
| Sonnet 5 | $0.00036 | $0.00345 |
| Haiku 4.5 | $0.00018 | $0.00172 |
Grade A, and why
alphagbm-buffett-analysis 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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- alphagbm-buffett-analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM Buffett Analysis
The 4 lenses Buffett himself says he applies, computed from yfinance fundamentals and returned as a single-number verdict plus reasoning for each lens.
The 4 Lenses
- Business (20% weight) — circle of competence. Simple sectors (consumer staples, utilities, industrials) score high. Complex sectors (tech, healthcare, financials) score lower unless mega-cap like AAPL.
- Moat (30% weight) — durable advantage. Gross margin > 40%, ROE > 20%, profit margin > 15%, and market cap > $100B each contribute to the moat score.
- Management (15% weight) — capital allocation proxy via dividend continuity
- payout ratio (15-60% is ideal balance) + 5yr avg div yield.
- Valuation (35% weight) — fair price check. PE < 15 → +20, PEG < 1 → +15, FCF yield > 10Y treasury + 2pp → +20. PE > 40 or PEG > 2.5 → deductions.
Overall Verdict
- ≥ 75 → HOLDABLE (color green) — meets Buffett standards, long-term hold
- 55-74 → WATCHABLE (color amber) — wait for better price or clearer evidence
- < 55 → AVOID (color red) — fails Buffett's standards
Why This Is a Separate Skill
The generic alphagbm-stock-analysis runs a G=B+M style/momentum score. Buffett's
framework is different — it weights moat + valuation much more heavily than
momentum, and penalizes complex businesses regardless of growth. This skill
codifies Buffett's rules, not AlphaGBM's house rules.
How to Use
Input:
ticker(required) — US stock symbol
Output:
scorecard.business:{score, sector, industry, verdict_zh, verdict_en}scorecard.moat:{score, gross_margin, roe, profit_margin, market_cap_b, reasons_zh, reasons_en}scorecard.management:{score, dividend_rate, payout_ratio, reasons_zh, reasons_en}scorecard.valuation:{score, pe, forward_pe, peg, pb, fcf_yield_pct, ten_year_treasury, reasons_zh, reasons_en}scorecard.overall:{score, verdict, verdict_zh, verdict_en, color}
Example Queries
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.
- 9d ago First seen · 152 lines · 180 tokens per session scan A f2ace7af2911
alphagbm-buffett-analysis is a skill published in the GitHub repository AlphaGBM/skills (2,240 stars, last pushed 2mo ago), licensed MIT. It adds 180 tokens to every session and 1,724 once invoked, about $0.0009 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.
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