tradermonty/claude-trading-skillsPublic

Claude Trading Skills started as a personal project to use AI to improve my own trading process.

Claude Trading Skills is a Claude Skills-based trading workflow toolkit for time-constrained individual investors.

It is designed for investors who use long-term investing, ETFs, and dividend stocks as their core, while using disciplined swing trading as a satellite strategy when market conditions are favorable.

The goal is not to outsource buy/sell decisions to AI. The goal is to structure market review, risk management, trade planning, journaling, and continuous improvement. It is open source because the workflows, checklists, and review habits behind better trading decisions can improve through shared practice.

This is not a signal service or a promise of profitability. It is a toolkit for traders who want to build a better decision process.

The project follows a first for self, open for others stance: it is built first as a practical workflow the author uses, then shared openly for others who face similar constraints.

📖 Documentation site: https://tradermonty.github.io/claude-trading-skills/

Project vision: PROJECT_VISION.md

日本語版READMEはREADME.ja.mdをご覧ください。

This repository is for educational, research, and process-improvement purposes only. It is not financial advice, investment advisory service, tax advice, legal advice, a signal service, or a broker execution platform. Trading and investing involve risk, including loss of principal. Past performance, backtests, screens, reports, and AI-generated analysis do not guarantee future results. All trading decisions, position sizing, tax/regulatory compliance, and broker usage are the user's responsibility.

The project is provided under the MIT License, AS IS, WITHOUT WARRANTY.

This repository is designed for:

• Time-constrained individual investors
• Long-term investors who also want disciplined swing-trading upside
• Dividend and ETF investors who want structured portfolio review
• Traders who want to manage risk before finding trade candidates
• Investors who want to journal and improve their decision process

It is not designed for fully automated trading, signal outsourcing, or short-term scalping.

New users should start with one of these operational workflows. Each link points to a machine-readable manifest under workflows/ that names the exact skills, decision gates, and artifacts in order.

See workflows/README.md for how to read a manifest and run it manually. For a one-page "which workflow fits my situation?" guide, see Find Your Workflow (日本語).

New here? Follow Your First Week (日本語) from installation through a no-paid-data-API market check, first journal entry, and first weekly review.

These visual excerpts use fictional data to show the kinds of results you can review before setting up a skill. They are illustrations, not live market readings or trading instructions.

Select a preview to open the full-size SVG; on a phone, zoom after opening.

Choose a workflow: Trading Skills Navigator recommends a starting path from your goal.

Review market risk: The market-regime-daily workflow ends with an Exposure Coach posture for human review, not an order.

Learn from closed trades: Weekly Performance Digest summarizes realized outcomes and process lessons.

Claude Web Skills are currently available on Free, Pro, Max, Team, and Enterprise accounts; see Anthropic's current Skills help because access can change. Claude Code has separate account requirements, and the Claude.ai Free plan does not include it; see the Claude Code setup guide. FMP, FINVIZ Elite, and Alpaca are optional data or broker integrations for specific workflows. The five-skill starter path below works with public CSVs, chart screenshots, and local files, so it does not require a paid market-data API subscription.

If you do not have FMP / FINVIZ / Alpaca subscriptions, start with these five skills and run them manually:

1. market-breadth-analyzer — public CSV breadth scoring; no API key
2. uptrend-analyzer — public CSV uptrend participation; no API key
3. position-sizer — pure calculation; no I/O
4. trader-memory-core — local YAML journaling
5. signal-postmortem — review framework

This path lets you review market conditions, size trades, journal decisions, and review outcomes without paid data APIs. Note: "no API" does not mean "no external data" — these skills still need public CSVs, chart screenshots, or local files. See each skill's integrations: entry in skills-index.yaml for exact input requirements.

Canonical source: skills-index.yaml is the authoritative index of all skills. If this README, CLAUDE.md, or docs disagree with the index, the index is correct. The same applies to multi-skill workflows — workflows/*.yaml is canonical.

• skills/<skill-name>/ – Source folder for each trading skill. Contains SKILL.md, reference material, and any helper scripts.
• skills-index.yaml – Canonical metadata index for every skill (id, category, integrations, workflows back-references).
• workflows/ – Operational workflow manifests for the Core + Satellite routines (canonical, validator-enforced via --strict-workflows).
• skill-packages/ – Pre-built .skill archives ready to upload to Claude's web app Skills tab.
• docs/ – Documentation site content, generated skill pages, and docs/dev/metadata-and-workflow-schema.md (schema spec).
• scripts/ – Repository-level automation, including the schema validator and one-shot bootstrap helper.
• skillsets/ – Purpose-specific install bundles defining required / recommended / optional skills for major goals (6 skillsets shipped: market-regime, core-portfolio, dividend-income, swing-opportunity, trade-memory, strategy-research; consumed by the Navigator).

First time here? Read the FAQ for plan, cost, safety, and scope answers.

See the Changelog for changes and the release compatibility and migration policy before updating an installed skill.

1. Open the latest GitHub Release and download the .skill asset that matches the skill you want, plus SHA256SUMS. Verify its hash as described in the release policy. If the Releases page is empty, follow the source-folder installation path below or build a local package with scripts/package_skills.py.
2. For an individual account, open Settings > Capabilities and enable Code execution and file creation. Team and Enterprise users may need an organization owner to enable Skills.
3. Open Customize > Skills, upload the ZIP, confirm it appears in the list, and enable it if needed (see Anthropic's current Skills help).

1. Clone or download this repository.
2. Copy the desired skill folder (e.g., backtest-expert) to ~/.claude/skills/ for personal use or .claude/skills/ inside a project (see the Claude Code setup guide).
3. Claude Code detects changes in an existing skills directory. Restart it only if you created the top-level skills directory after the session started.

Tip: .skill packages are built from the source folders with tests and local build artifacts omitted. Edit a source folder if you want to customize a skill, then run python3 scripts/package_skills.py --skill <skill-name> before uploading to the web app.

The shared project environment supports Python 3.11 to 3.13 on Linux, Windows, and macOS. Packaged standalone skills retain a Python 3.9 source floor. A root-project combination counts as supported only if it is exercised by the project's CI; other combinations in that range are best-effort and unverified. See docs/dev/compatibility-matrix.md for the exact supported matrix and how it is enforced.

Want a ready-to-run agent-style workflow? See the companion Hermes Trading Research Agent Work Package.

It packages these skills into a Hermes profile with task-oriented slash-command routines such as /pre-market-routine, /after-close-review, /trade-journal, /weekly-portfolio-review, and /monthly-performance-review.

This is a research, journaling, and risk-review assistant, not an automated trading system. It does not place orders, provide a signal service, or run hidden scheduled jobs; human decision gates remain central.

This repository contains skills across the following areas:

The detailed catalog below is auto-generated from skills-index.yaml by scripts/generate_catalog_from_index.py. To update a skill's description, edit its skills-index.yaml entry and re-run the generator (python3 scripts/generate_catalog_from_index.py). For a more navigable version, use the documentation site. For an auto-generated overview of skill lifecycle, test coverage, and runtime health, see the Quality Dashboard.

The main Core + Satellite starting path is described above. The examples below show additional ways to compose skills, including advanced satellite and contributor workflows.

1. Use Economic Calendar Fetcher to check today's high-impact events (FOMC, NFP, CPI releases)
2. Use Earnings Calendar to identify major companies reporting today
3. Use Market News Analyst to review overnight developments and their market impact
4. Use Breadth Chart Analyst to assess overall market health and positioning

1. Use Sector Analyst to fetch CSV data and identify rotation patterns (optionally provide charts)
2. Use Technical Analyst on key indices and positions for trend confirmation
3. Use Market Environment Analysis for comprehensive macro briefing
4. Use US Market Bubble Detector to assess speculative excess and risk levels

1. Use US Stock Analysis for comprehensive fundamental and technical review
2. Use Earnings Calendar to check upcoming earnings dates
3. Use Market News Analyst to review recent company-specific news and sector developments
4. Use Backtest Expert to validate entry/exit strategies before position sizing

1. Use Stanley Druckenmiller Investment for macro theme identification
2. Use Economic Calendar Fetcher to time entries around major data releases
3. Use Breadth Chart Analyst and Technical Analyst for confirmation signals
4. Use US Market Bubble Detector for risk management and profit-taking guidance

1. Use Earnings Trade Analyzer to score recent earnings reactions (gap size, trend, volume, MA position)
2. Use PEAD Screener (Mode B) with analyzer output to find PEAD setups (red candle pullbacks → breakout signals)
3. Use Technical Analyst to confirm weekly chart patterns and support/resistance levels
4. Use Liquidity filters in PEAD Screener to ensure position sizing feasibility
5. Monitor SIGNAL_READY stocks for breakout entries with defined stop-loss (red candle low) and 2R targets

1. Use Value Dividend Screener to identify high-quality dividend stocks with sustainable yields
2. Use Dividend Growth Pullback Screener to find growth-focused dividend stocks at attractive technical entry points
3. Use US Stock Analysis for deep-dive fundamental analysis on top candidates
4. Use Earnings Calendar to track upcoming earnings for portfolio holdings
5. Use Market Environment Analysis to assess macro conditions for dividend strategies
6. Use Backtest Expert to validate dividend capture or growth strategies

1. Use Kanchi Dividend SOP to run Kanchi's 5-step process and create buy plans with invalidation conditions
2. Use Kanchi Dividend Review Monitor on a daily/weekly/quarterly cadence to generate OK/WARN/REVIEW queues
3. Use Kanchi Dividend US Tax Accounting to align holdings with qualified-dividend assumptions and account location
4. Feed REVIEW findings back into Kanchi Dividend SOP before adding to positions

1. Use Options Strategy Advisor to simulate and compare options strategies using Black-Scholes pricing
2. Use Technical Analyst to identify optimal entry timing and support/resistance levels
3. Use Earnings Calendar to plan earnings-based options strategies
4. Use US Stock Analysis to validate fundamental thesis before deploying capital
5. Review Greeks and P/L scenarios to select optimal strategy (covered calls, spreads, straddles, etc.)

1. Use Portfolio Manager to fetch current holdings via Alpaca MCP and analyze portfolio health
2. Review asset allocation, sector diversification, and risk metrics (beta, volatility, concentration)
3. Review position-level flags (HOLD/ADD/TRIM/SELL candidates) based on thesis validation
4. Use Market Environment Analysis and US Market Bubble Detector to assess macro conditions
5. Review a rebalancing plan and decide manually which actions, if any, to take

1. Use Pair Trade Screener to identify cointegrated stock pairs within sectors
2. Analyze mean-reversion metrics (half-life, z-score) and hedge ratios
3. Use Technical Analyst to confirm technical setups for both legs of the pair
4. Monitor entry/exit signals based on z-score thresholds
5. Track spread convergence and manage market-neutral positions

• Data Quality Checker (data-quality-checker)

  • Validates data quality in market analysis documents and blog articles before publication.
  • 5 check categories: price scale inconsistencies (ETF vs futures digit hints), instrument notation consistency, date/weekday mismatches (English + Japanese), allocation total errors (section-limited), and unit mismatches.
  • Advisory mode — flags issues as warnings for human review, exit 0 even with findings.
  • Supports full-width Japanese characters (％, 〜), range notation (50-55%), and year inference for dates without explicit year.
  • No API key required — works offline on local markdown files.

• Skill Designer (skill-designer)

  • Generates Claude CLI prompts for designing new skills from structured idea specifications.
  • Embeds repository conventions (structure guide, quality checklist, SKILL.md template) into the prompt.
  • Lists existing skills to prevent duplication. Used by the skill auto-generation pipeline's daily flow.
  • No API key required.

• Dual-Axis Skill Reviewer (dual-axis-skill-reviewer)

  • Reviews skill quality using a dual-axis method: deterministic auto scoring (structure, workflow, execution safety, artifacts, tests) and optional LLM deep review.
  • 5-category auto axis (0-100): Metadata & Use Case (20), Workflow Coverage (25), Execution Safety & Reproducibility (25), Supporting Artifacts (10), Test Health (20).
  • Detects knowledge_only skills (no scripts, references only) and adjusts scoring expectations to avoid unfair penalties.
  • Optional LLM axis for qualitative review (correctness, risk, missing logic, maintainability) with configurable weight blending.
  • Supports --all flag to review every skill at once, --skip-tests for quick triage, and --project-root for cross-project review.
  • No API key required.

• Skill Idea Miner (skill-idea-miner)

  • Mines Claude Code session logs for skill idea candidates, scores them for novelty/feasibility/trading value, and maintains a prioritized backlog.
  • Used by the weekly skill auto-generation pipeline. Can also be run manually.
  • No API key required.

The self-improvement and skill-generation pipelines are maintainer workflows, not beginner trading steps. See the Skill Automation Quickstart (日本語) for behavior, side effects, manual commands, and macOS scheduling.

• Update SKILL.md files to tweak trigger descriptions or capability notes; ensure the frontmatter name matches the folder name when zipping.
• Extend reference documents or add scripts inside each skill folder to support new workflows.
• When distributing updates, regenerate the matching .skill file in skill-packages/ so web-app users get the latest version:

  python3 scripts/package_skills.py --skill <skill-name>

Several skills require API keys for data access:

For each skill's FMP, FINVIZ Elite and Alpaca requirements, see the API Requirements Matrix, generated from skills-index.yaml. Check the individual skill guides for mode-specific prerequisites and offline alternatives before configuring credentials.

Financial Modeling Prep (FMP) API:

• Free tier: 250 requests/day (sufficient for most use cases)
• Sign up: https://financialmodelingprep.com/developer/docs
• Set environment variable: export FMP_API_KEY=your_key_here
• Or provide key via command-line argument when prompted

FINVIZ Elite API:

• Subscription: $39.50/month or $299.50/year
• Sign up: https://elite.finviz.com/
• Set environment variable: export FINVIZ_API_KEY=your_key_here
• Provides fast pre-screening for dividend screeners

Alpaca Trading API:

• Free paper trading account available
• Sign up: https://alpaca.markets/
• Requires Alpaca MCP Server configuration
• Set environment variables:

  export ALPACA_API_KEY="your_api_key_id"
  export ALPACA_SECRET_KEY="your_secret_key"
  export ALPACA_PAPER="true"  # or "false" for live trading

• Claude Skills launch overview: https://www.anthropic.com/news/skills
• Claude Code Skills how-to: https://docs.claude.com/en/docs/claude-code/skills
• Financial Modeling Prep API: https://financialmodelingprep.com/developer/docs

Questions or suggestions? Open an issue or include guidance alongside the relevant skill folder so future users know how to get the most from these trading assistants.

All skills and reference materials in this repository are provided for educational and research purposes.