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 leecyno1/boutique-skills --skill alphagbm-buffett-analysisgit clone --depth 1 https://github.com/leecyno1/boutique-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/leecyno1/boutique-skills/alphagbm-buffett-analysis)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-buffett-analysis"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-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/leecyno1/boutique-skills/alphagbm-buffett-analysis"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-buffett-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 11d 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.
This is a copy
100% identical to alphagbm-buffett-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 ships with it
17 files 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.
- LICENSE 1.0 KB
- mock-data/AAPL.json 6.7 KB
- mock-data/buffett-analysis/example-ko.json 1.9 KB
- mock-data/fear-score/example-calm.json 661 B
- mock-data/fear-score/example-signal-triggered.json 663 B
- mock-data/hedge-advisor/example-gain-protection.json 1.5 KB
- mock-data/marks-cycle/example-neutral.json 366 B
- mock-data/META.json 8.8 KB
- mock-data/NVDA.json 8.4 KB
- mock-data/SPY.json 7.5 KB
- mock-data/take-profit/example-leveraged-etf.json 1.1 KB
- mock-data/tepper-signal/example-armed.json 589 B
- mock-data/tepper-signal/example-cold.json 488 B
- mock-data/TSLA.json 9.4 KB
- mock-data/vix-status/example-extreme-fear.json 528 B
- mock-data/vix-status/example-sweet-spot.json 506 B
- SOURCE.txt 455 B
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.
- 11d ago First seen · 152 lines · 180 tokens per session scan A f2ace7af2911
alphagbm-buffett-analysis is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed 20d 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. It is 100% identical to alphagbm-buffett-analysis, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.