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Alphagbm Buffett Analysis

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AlphaGBM
alphagbm-buffett-analysis

Warren Buffett-lens scorecard for any ticker. Scores 4 dimensions 0-100 each (business / circle of competence, moat / durable advantage, management / capital allocation, valuation / fair price vs 10Y treasury) and returns a weighted overall HOLDABLE / WATCHABLE / AVOID verdict. This is NOT a generic fundamental screener — it's Buffett's specific framework mechanically applied: sector simplicity, gross margin + ROE + profit margin thresholds, FCF yield vs treasury, and dividend-continuity as management proxy. Triggers: "Buffett analysis AAPL", "score KO with Buffett lens", "would Buffett buy MSFT", "JNJ Buffett scorecard", "AAPL moat analysis", "fair price vs bonds", "Buffett-style verdict on NVDA", "long-term hold analysis"

Overview

PublisherAlphaGBM
Repositoryskills
Skill namealphagbm-buffett-analysis
Stars
2.7K
Forks
290
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AlphaGBM on GitHub. Read the source before you install it.

Installation

Install the Alphagbm Buffett Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AlphaGBM/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/alphagbm-buffett-analysis .claude/skills/alphagbm-buffett-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alphagbm Buffett Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alphagbm Buffett Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alphagbm Buffett Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

  1. 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.
  2. Moat (30% weight) — durable advantage. Gross margin > 40%, ROE > 20%, profit margin > 15%, and market cap > $100B each contribute to the moat score.
  3. Management (15% weight) — capital allocation proxy via dividend continuity
    • payout ratio (15-60% is ideal balance) + 5yr avg div yield.
  4. 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

  • Buffett analysis on KO → likely HOLDABLE (simple business, strong moat, 30+ year hold by Buffett himself)
  • would Buffett buy NVDA → likely WATCHABLE or AVOID (complex sector, high valuation)
  • Buffett scorecard JNJ → likely HOLDABLE (consumer defensive, strong margins, reasonable PE)
  • score AAPL with Buffett lens → reference Berkshire's own holding for context
  • apply Buffett's checklist to WMT → retail-native test case

Mock Data

Mock data in mock-data/buffett-analysis/ — sample for KO (HOLDABLE).

API Endpoint

POST /api/masters/buffett-analyze
Content-Type: application/json

Request body:

json
{"ticker": "KO"}

Response shape:

json
{
  "success": true,
  "ticker": "KO",
  "current_price": 63.4,
  "scorecard": {
    "business": {
      "score": 85,
      "sector": "Consumer Defensive",
      "industry": "Beverages - Non-Alcoholic",
      "verdict_zh": "业务相对简单,在巴菲特能力圈范围内",
      "verdict_en": "Relatively simple business within Buffett's circle"
    },
    "moat": {
      "score": 100,
      "gross_margin": 60.3,
      "roe": 41.8,
      "profit_margin": 22.4,
      "market_cap_b": 273.4,
      "reasons_zh": ["毛利率 60.3% > 40%,显示定价权", "ROE 41.8% > 20%,资本效率强", "市值 $273B > $100B,规模壁垒", "净利率 22.4% > 15%,强定价权"],
      "reasons_en": ["Gross margin 60.3% > 40% shows pricing power", "ROE 41.8% > 20% — strong capital efficiency", "Market cap $273B > $100B — scale moat", "Net margin 22.4% > 15% — strong pricing power"]
    },
    "management": {
      "score": 80,
      "dividend_rate": 1.94,
      "payout_ratio": 77.0,
      "reasons_zh": ["派息 $1.94 — 体现向股东返现意愿", "5 年平均股息率 3.1%"],
      "reasons_en": ["Dividend $1.94 — willingness to return cash", "5-yr avg div yield 3.1%"]
    },
    "valuation": {
      "score": 45,
      "pe": 24.8,
      "forward_pe": 22.1,
      "peg": 3.2,
      "pb": 10.5,
      "fcf_yield_pct": 3.5,
      "ten_year_treasury": 4.3,
      "reasons_zh": ["FCF 收益率 3.5% < 10Y 美债 4.3%,不如债券"],
      "reasons_en": ["FCF yield 3.5% < 10Y 4.3% — bonds beat it"]
    },
    "overall": {
      "score": 78.3,
      "verdict": "HOLDABLE",
      "verdict_zh": "符合巴菲特标准 — 值得长期持有",
      "verdict_en": "Meets Buffett standards — worth a long-term hold",
      "color": "green"
    }
  },
  "timestamp": "2026-04-24T08:00:00"
}

Pricing: 1 stock-analysis credit per call; 30-min cache per ticker (cache hits free).

Related Skills

SkillRelevance
alphagbm-stock-analysisHouse G=B+M model — complementary, different weights
alphagbm-company-profileDeep fundamental profile once Buffett flags HOLDABLE
alphagbm-investment-thesisTurn Buffett verdict into a trackable thesis

Powered by AlphaGBM — Real-data options & research intelligence. 10K+ users.

Frequently asked questions

What does the Alphagbm Buffett Analysis AI skill do?

Warren Buffett-lens scorecard for any ticker. Scores 4 dimensions 0-100 each (business / circle of competence, moat / durable advantage, management / capital allocation, valuation / fair price vs 10Y treasury) and returns a weighted overall HOLDABLE / WATCHABLE / AVOID verdict. This is NOT a generic fundamental screener — it's Buffett's specific framework mechanically applied: sector simplicity, gross margin + ROE + profit margin thresholds, FCF yield vs treasury, and dividend-continuity as management proxy. Triggers: "Buffett analysis AAPL", "score KO with Buffett lens", "would Buffett buy...

Why use Alphagbm Buffett Analysis on TypingMind?

Because you install it once and use it with any model. Alphagbm Buffett Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alphagbm Buffett Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlphaGBM/skills/tree/main/skills/alphagbm-buffett-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Alphagbm Buffett Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alphagbm Buffett Analysis?

As many as you like. As long as a model supports skills, you can use Alphagbm Buffett Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alphagbm Buffett Analysis AI skill free?

Yes. It is published on GitHub by AlphaGBM under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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