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Claude Trading Skills

Curated Claude skills for equity investors and traders. Each skill bundles prompts, knowledge, and optional helper scripts so Claude can assist with systematic backtesting, market analysis, technical charting, economic calendar monitoring, and US stock research. The repository packages skills for both Claude's web app and Claude Code workflows.

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

日本語版READMEはREADME.ja.mdをご覧ください。

Repository Layout

  • <skill-name>/ – Source folder for each trading skill. Contains SKILL.md, reference material, and any helper scripts.
  • skill-packages/ – Pre-built .skill archives ready to upload to Claude's web app Skills tab.

Getting Started

Use with Claude Web App

  1. Download the .skill file that matches the skill you want from skill-packages/.
  2. Open Claude in your browser, go to Settings → Skills, and upload the ZIP (see Anthropic's Skills launch post for feature overview).
  3. Enable the skill inside the conversation where you need it.

Use with Claude Code (desktop or CLI)

  1. Clone or download this repository.
  2. Copy the desired skill folder (e.g., backtest-expert) into your Claude Code Skills directory (open Claude Code → Settings → Skills → Open Skills Folder, per the Claude Code Skills documentation).
  3. Restart or reload Claude Code so the new skill is detected.

Tip: The source folders and ZIPs contain identical content. Edit a source folder if you want to customize a skill, then re-zip it before uploading to the web app.

Skill Catalog

Market Analysis & Research

  • Sector Analyst (sector-analyst)

    • Fetches sector uptrend ratio data from CSV (no API key required) and analyzes sector rotation patterns based on market cycle theory.
    • Calculates cyclical vs defensive risk regime scores, identifies overbought/oversold sectors, and estimates the current market cycle phase (Early/Mid/Late Cycle or Recession).
    • Optionally accepts chart images for supplementary industry-level analysis.
    • Generates scenario-based probability assessments for sector rotation strategies.

  • Breadth Chart Analyst (breadth-chart-analyst)

    • Analyzes S&P 500 Breadth Index and US Stock Market Uptrend Stock Ratio charts to assess market health and positioning.
    • Provides medium-term strategic and short-term tactical market outlook based on breadth indicators.
    • Identifies bull market phases (Healthy Breadth, Narrowing Breadth, Distribution) and bear market signals.
    • Includes detailed breadth interpretation framework and historical pattern references.

  • Technical Analyst (technical-analyst)

    • Analyzes weekly price charts for stocks, indices, cryptocurrencies, and forex pairs using pure technical analysis.
    • Identifies trends, support/resistance levels, chart patterns, and momentum indicators without fundamental bias.
    • Generates scenario-based probability assessments with specific trigger levels for trend changes.
    • References cover Elliott Wave, Dow Theory, Japanese candlesticks, and technical indicator interpretation.

  • Market News Analyst (market-news-analyst)

    • Analyzes recent market-moving news events from the past 10 days using automated WebSearch/WebFetch collection.
    • Focuses on FOMC decisions, central bank policy, mega-cap earnings, geopolitical events, and commodity market drivers.
    • Produces impact-ranked reports using quantitative scoring framework (Price Impact × Breadth × Forward Significance).
    • References include trusted news sources guide, event pattern analysis, and geopolitical-commodity correlations.

  • US Stock Analysis (us-stock-analysis)

    • Comprehensive US equity research assistant covering fundamentals, technicals, peer comparisons, and investment memo generation.
    • Analyzes financial metrics, valuation ratios, growth trajectories, and competitive positioning.
    • Generates structured investment memos with bull/bear cases and risk assessments.
    • Reference library documents analytical frameworks (fundamental-analysis.md, technical-analysis.md, financial-metrics.md, report-template.md).

  • Market Environment Analysis (market-environment-analysis)

    • Guides Claude through comprehensive global macro briefings covering equity indices, FX, commodities, yields, and market sentiment.
    • Provides structured reporting templates for daily/weekly market reviews with indicator-based assessments.
    • Includes indicator cheat sheets (references/indicators.md) and analysis patterns.
    • Helper script scripts/market_utils.py assists with report formatting and data visualization.

  • Market Breadth Analyzer (market-breadth-analyzer)

    • Quantifies market breadth health using TraderMonty's public CSV data with a data-driven 6-component scoring system (0-100).
    • Components: Overall Breadth, Sector Participation, Sector Rotation, Momentum, Mean Reversion Risk, and Historical Context.
    • Measures how broadly the market is participating in a rally or decline (100 = maximum health, 0 = critical weakness).
    • No API key required - uses freely available CSV data from GitHub.

  • Uptrend Analyzer (uptrend-analyzer)

    • Diagnoses market breadth health using Monty's Uptrend Ratio Dashboard, tracking ~2,800 US stocks across 11 sectors.
    • 5-component composite scoring (0-100): Market Breadth, Sector Participation, Sector Rotation, Momentum, Historical Context.
    • Warning overlay system: Late Cycle and High Selectivity flags tighten exposure guidance and add cautionary actions.
    • Sector-level fallback: automatically constructs sector summary from timeseries data when sector_summary.csv is unavailable.
    • No API key required - uses free GitHub CSV data.

  • Macro Regime Detector (macro-regime-detector)

    • Detects structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis.
    • 6-component analysis: RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation.
    • Identifies regimes: Concentration, Broadening, Contraction, Inflationary, and Transitional states.
    • FMP API required for cross-asset ETF data (RSP, SPY, IWM, HYG, LQD, TLT, XLE, XLU, etc.).

  • Institutional Flow Tracker (institutional-flow-tracker)

    • Tracks institutional investor ownership changes using 13F SEC filings data to identify "smart money" accumulation and distribution patterns.
    • Screens stocks with significant institutional ownership changes (>10-15% QoQ) and analyzes multi-quarter trends.
    • Tier-based quality framework weights superinvestors (Berkshire, Baupost) 3.0-3.5x vs index funds 0.0-0.5x.
    • Deep dive analysis on individual stocks: quarterly ownership trends, top holders, new/increased/decreased/closed positions.
    • Concentration risk analysis and position change categorization (new buyers, increasers, decreasers, exits).
    • FMP API integration with free tier sufficient for quarterly portfolio reviews (250 calls/day).
    • Follow specific institutions like Warren Buffett (Berkshire), Cathie Wood (ARK), Bill Ackman (Pershing Square).
    • Comprehensive reference guides: 13F filings, institutional investor types, interpretation framework with signal strength matrix.

  • Theme Detector (theme-detector)

    • Detects trending market themes (bullish and bearish) by analyzing FINVIZ industry/sector performance across multiple timeframes.
    • 3-dimensional scoring: Theme Heat (0-100, momentum/volume/uptrend/breadth), Lifecycle Maturity (0-100, duration/RSI extremity/price extremes/valuation/ETF proliferation), and Confidence (Low/Medium/High).
    • Direction-aware analysis: bearish themes scored with equal sensitivity as bullish themes using inverted indicators.
    • Cross-sector theme detection (AI/Semis, Clean Energy, Gold, Cybersecurity, etc.) and vertical sector concentration identification.
    • Lifecycle stages: Emerging, Accelerating, Trending, Mature, Exhausting — with representative stocks and proxy ETFs per theme.
    • Integrates Monty's Uptrend Ratio Dashboard as supplementary breadth signal (3-point evaluation: ratio + MA10 + slope).
    • No API key required for core functionality (FINVIZ public + yfinance). FMP/FINVIZ Elite optional for enhanced stock selection.

Economic & Earnings Calendars

  • Economic Calendar Fetcher (economic-calendar-fetcher)

    • Fetches upcoming economic events using Financial Modeling Prep (FMP) API for next 7-90 days.
    • Retrieves central bank decisions, employment reports (NFP), inflation data (CPI/PPI), GDP releases, and other market-moving indicators.
    • The script outputs raw JSON or text; the assistant filters events and generates a Markdown report with impact assessment (High/Medium/Low) and market implications analysis.
    • Supports flexible API key management (environment variable recommended; --api-key CLI argument as fallback).

  • Earnings Calendar (earnings-calendar)

    • Retrieves upcoming earnings announcements for US stocks using FMP API with focus on mid-cap+ companies (>$2B market cap).
    • Organizes earnings by date and timing (Before Market Open, After Market Close, During Market Hours).
    • Provides clean markdown table format for weekly earnings review and portfolio monitoring.
    • Flexible API key management supporting CLI, Desktop, and Web environments.

Strategy & Risk Management

  • Scenario Analyzer (scenario-analyzer)

    • Analyzes news headlines to build 18-month scenario projections with sector impacts and stock picks.
    • Dual-agent architecture: scenario-analyst for primary analysis, strategy-reviewer for second opinion.
    • Generates comprehensive reports including 1st/2nd/3rd order effects, recommended tickers, and critical review.
    • No API key required - uses WebSearch for news gathering.

  • Backtest Expert (backtest-expert)

    • Framework for professional-grade strategy validation with hypothesis definition, parameter robustness checks, and walk-forward testing.
    • Emphasizes realistic assumptions: slippage modeling, transaction costs, survivorship bias elimination, and out-of-sample validation.
    • References cover detailed methodology (references/methodology.md) and failure post-mortems (references/failed_tests.md).
    • Guides systematic approach from idea generation through production deployment with quality gates.

  • Stanley Druckenmiller Investment Advisor (stanley-druckenmiller-investment)

    • Encodes Druckenmiller's investment philosophy for macro positioning, liquidity analysis, and asymmetric risk/reward assessment.
    • Focuses on "bet big when you have high conviction" approach with strict loss-cutting discipline.
    • Reference pack provides philosophy deep dives, market analysis workflows, and historical case studies (content in Japanese and English).
    • Emphasizes macro theme identification, technical confirmation, and position sizing strategies.

  • US Market Bubble Detector (us-market-bubble-detector) - v2.1 Updated

    • Data-driven bubble risk assessment using revised Minsky/Kindleberger framework with mandatory quantitative metrics (Put/Call, VIX, margin debt, breadth, IPO data).
    • Two-phase evaluation: Quantitative scoring (0-12 points) → Strict qualitative adjustment (0-3 points, reduced from +5 in v2.0).
    • Confirmation bias prevention with measurable evidence requirements for all qualitative adjustments.
    • Granular risk phases: Normal (0-4) → Caution (5-7) → Elevated Risk (8-9) → Euphoria (10-12) → Critical (13-15).
    • Actionable risk budgets and profit-taking strategies for each phase with specific short-selling criteria.
    • Supplemented by historical case files, quick-reference checklists (JP/EN), and implementation guide with strict scoring criteria.

  • Options Strategy Advisor (options-strategy-advisor)

    • Educational options trading tool providing theoretical pricing, strategy analysis, and risk management guidance using Black-Scholes model.
    • Calculates all Greeks (Delta, Gamma, Theta, Vega, Rho) and supports 17+ options strategies (covered calls, spreads, iron condors, straddles, etc.).
    • Uses FMP API for free stock data + Black-Scholes pricing to simulate strategies without expensive real-time options data ($99-500/month).
    • P/L simulation and visualization for comparing strategies side-by-side with earnings strategy integration.
    • Theoretical prices approximate market mid-prices; users can input actual IV from broker for better accuracy.
    • Ideal for learning options mechanics, understanding Greeks, and strategy planning before live trading.

  • Portfolio Manager (portfolio-manager)

    • Comprehensive portfolio analysis and management with Alpaca MCP Server integration for real-time holdings data.
    • Multi-dimensional analysis: Asset allocation, sector diversification, risk metrics (beta, volatility, drawdown), and performance review.
    • Position-level evaluation with HOLD/ADD/TRIM/SELL recommendations based on thesis validation and valuation.
    • Generates detailed rebalancing plans with specific actions to optimize portfolio allocation toward target models.
    • Supports model portfolios (Conservative/Moderate/Growth/Aggressive) for benchmark comparison.
    • Requires Alpaca brokerage account (paper or live) and configured Alpaca MCP Server; manual data entry also supported.

  • Position Sizer (position-sizer)

    • Calculates risk-based position sizes for long stock trades using Fixed Fractional, ATR-based, and Kelly Criterion methods.
    • Applies portfolio constraints (max position %, max sector %) and identifies binding constraints.
    • Two output modes: "shares" mode (with entry/stop) returns final recommended share count; "budget" mode (Kelly only) returns recommended risk budget.
    • Generates JSON + markdown reports with calculation details, constraint analysis, and final recommendations.
    • No API key required — pure calculation, works offline.

  • Edge Candidate Agent (edge-candidate-agent)

    • Converts daily market observations into reproducible research tickets and exports Phase I-compatible candidate specs for trade-strategy-pipeline.
    • Generates strategy.yaml + metadata.json artifacts from structured research tickets with interface contract validation (edge-finder-candidate/v1).
    • Supports two entry families: pivot_breakout (with VCP detection) and gap_up_continuation (with gap detection).
    • Includes preflight validation against pipeline schema with uv run subprocess fallback for cross-environment compatibility.
    • Guardrails enforce schema bounds (risk limits, exit rules, non-empty conditions) and deterministic metadata with interface versioning.
    • No API key required — operates on local YAML files and validates against local pipeline repository.

  • Trade Hypothesis Ideator (trade-hypothesis-ideator)

    • Generates 1-5 falsifiable hypothesis cards from structured strategy context, market context, trade logs, and journal evidence.
    • Two-pass workflow: Pass 1 builds evidence_summary.json; Pass 2 validates raw hypotheses, ranks cards, and emits JSON + markdown reports.
    • Guardrails enforce field completeness, banned phrase detection, duplicate detection, and constraint-violation checks.
    • Exports pursue hypotheses to strategy.yaml + metadata.json compatible with edge-finder-candidate/v1 (pivot_breakout, gap_up_continuation only).
    • No API key required — runs entirely on local JSON/YAML artifacts.

  • Strategy Pivot Designer (strategy-pivot-designer)

    • Detects backtest iteration stagnation and generates structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
    • Four deterministic triggers: improvement plateau, overfitting proxy, cost defeat, and tail risk — mapped from evaluate_backtest.py output.
    • Three pivot techniques: assumption inversion, archetype switch, and objective reframe across 8 canonical strategy archetypes.
    • Novelty scoring via Jaccard distance with deterministic tiebreaks ensures reproducible proposal ranking.
    • Outputs strategy_draft-compatible YAML with pivot_metadata extension; exportable drafts include candidate-agent ticket YAML.
    • No API key required — operates on local JSON/YAML files from backtest-expert and edge-strategy-designer.

  • Edge Strategy Reviewer (edge-strategy-reviewer)

    • Deterministic quality gate for strategy drafts produced by edge-strategy-designer.
    • Evaluates 8 criteria (C1-C8): edge plausibility, overfitting risk, sample adequacy, regime dependency, exit calibration, risk concentration, execution realism, and invalidation quality.
    • Weighted scoring (0-100) with PASS/REVISE/REJECT verdicts and export eligibility determination.
    • Precise threshold detection penalizes curve-fitted conditions; annual opportunity estimation flags overly restrictive strategies.
    • REVISE verdicts include concrete revision instructions for the feedback loop.
    • No API key required — operates on local YAML files from edge-strategy-designer.

  • Edge Pipeline Orchestrator (edge-pipeline-orchestrator)

    • Orchestrates the full edge research pipeline end-to-end: auto-detection, hints, concept synthesis, strategy design, critical review, and export.
    • Review-revision feedback loop (max 2 iterations): PASS/REJECT accumulated across iterations, REVISE drafts revised and re-reviewed, remaining REVISE downgraded to research_probe.
    • Export eligibility gate: only PASS + export_ready_v1 + exportable entry family drafts proceed to candidate export.
    • All upstream skills called via subprocess (no cross-skill imports) with pipeline manifest tracking full execution trace.
    • Supports resume-from-drafts, review-only, and dry-run modes.
    • No API key required — orchestrates local YAML/JSON files across edge skills.

  • Edge Signal Aggregator (edge-signal-aggregator)

    • Aggregates outputs from edge-candidate-agent, edge-concept-synthesizer, theme-detector, sector-analyst, institutional-flow-tracker, and edge-hint-extractor.
    • Applies configurable weighting, signal deduplication, recency adjustment, and contradiction handling to produce a ranked conviction dashboard.
    • Supports multiple upstream schema variants (for example priority_score, support.avg_priority_score, themes.all, heat/theme_heat) for robust cross-skill integration.
    • Exports JSON + markdown reports with provenance (contributing_skills), contradiction logs, and deduplication logs.
    • No API key required — operates on local JSON/YAML outputs from upstream edge skills.

  • Trader Memory Core (trader-memory-core)

    • Persistent state layer that tracks investment theses from screening idea to closed position with postmortem.
    • Bundles screener → analysis → position sizing → portfolio management outputs into a single thesis object.
    • Supports lifecycle management (IDEA → ENTRY_READY → ACTIVE → CLOSED), position attachment, review scheduling, and MAE/MFE analysis.
    • Integrates with kanchi-dividend-sop, earnings-trade-analyzer, vcp-screener, pead-screener, canslim-screener, and edge-candidate-agent.

  • Exposure Coach (exposure-coach)

    • Synthesizes outputs from market-breadth-analyzer, uptrend-analyzer, macro-regime-detector, market-top-detector, ftd-detector, theme-detector, sector-analyst, and institutional-flow-tracker into a unified exposure decision.
    • Answers the core question: "How much capital should I commit to equities right now?" before any individual stock analysis.
    • Generates a one-page Market Posture summary with exposure ceiling (0-100%), growth-vs-value bias, participation breadth assessment, and actionable recommendation (NEW_ENTRY_ALLOWED / REDUCE_ONLY / CASH_PRIORITY).
    • Accepts partial inputs — missing upstream files reduce confidence level but do not block execution.
    • FMP API key optional (needed only when institutional-flow-tracker data is included).

  • Signal Postmortem (signal-postmortem)

    • Records and analyzes post-trade outcomes for signals generated by edge pipeline, screeners, and other skills.
    • Classifies outcomes into TRUE_POSITIVE, FALSE_POSITIVE, MISSED_OPPORTUNITY, or REGIME_MISMATCH categories.
    • Generates weight adjustment feedback for edge-signal-aggregator and skill improvement backlog entries.
    • Supports batch processing of matured signals (5-day and 20-day holding periods) and manual outcome recording.
    • Aggregate statistics by skill, ticker, and time period for periodic signal quality audits.
    • FMP API key optional (for fetching realized returns; manual price entry also supported).

Market Timing & Bottom Detection

  • Market Top Detector (market-top-detector)

    • Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Rotation.
    • 6-component tactical timing system for identifying distribution and topping patterns.

  • Downtrend Duration Analyzer (downtrend-duration-analyzer)

    • Analyzes historical downtrend durations (peak-to-trough) and generates interactive HTML histograms segmented by sector and market cap.
    • Rolling window peak/trough detection with configurable depth and duration filters.
    • FMP API required.

  • FTD Detector (ftd-detector)

    • Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology.
    • Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring.
    • Complementary to Market Top Detector: this skill is offensive (bottom confirmation) while Market Top Detector is defensive (distribution detection).
    • Generates quality score (0-100) with exposure guidance for re-entering the market after corrections.
    • FMP API required for index price data.

Earnings Momentum Screening

  • Earnings Trade Analyzer (earnings-trade-analyzer)

    • Scores recent post-earnings stocks using a 5-factor weighted system: Gap Size (25%), Pre-Earnings Trend (30%), Volume Trend (20%), MA200 Position (15%), MA50 Position (10%).
    • Assigns A/B/C/D grades (A: 85+, B: 70-84, C: 55-69, D: <55) with composite score 0-100.
    • BMO/AMC timing-aware gap calculation — different base prices depending on when earnings were announced.
    • Optional entry quality filter excludes low-win-rate patterns (low price range, extreme gap + high score combinations).
    • API call budget management with --max-api-calls flag (default: 200) and automatic candidate trimming.
    • Outputs JSON with schema_version: "1.0" for downstream consumption by PEAD Screener.
    • FMP API required (free tier sufficient for typical 2-day lookback screening).

  • PEAD Screener (pead-screener)

    • Screens post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns using weekly candle analysis.
    • Stage-based monitoring: MONITORING → SIGNAL_READY (red candle found) → BREAKOUT (price breaks above red candle high) → EXPIRED (>5 weeks).
    • 4-component scoring: Setup Quality (30%), Breakout Strength (25%), Liquidity (25%), Risk/Reward (20%).
    • Two input modes: Mode A (FMP earnings calendar, standalone) and Mode B (earnings-trade-analyzer JSON output, pipeline).
    • Weekly candle aggregation using ISO week (Monday start) with earnings week splitting and partial week handling.
    • Liquidity filters: ADV20 >= $25M, avg volume >= 1M shares, price >= $10.
    • Trade setup output: entry price, stop (red candle low), target (2R), risk/reward ratio.
    • FMP API required (free tier sufficient for 14-day lookback screening).

Stock Screening & Selection

  • VCP Screener (vcp-screener)

    • Screens S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP).
    • Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points.
    • Two-axis scoring: separates pattern quality from execution readiness (state caps prevent chasing extended stocks).
    • Multi-stage filtering: Trend Template → VCP Base Detection → Contraction Analysis → Pivot Point Calculation.
    • FMP API required (free tier sufficient for default screening of top 100 candidates).

  • CANSLIM Stock Screener (canslim-screener) - Phase 2

    • Screens US stocks using William O'Neil's proven CANSLIM growth stock methodology for identifying multi-bagger candidates.
    • Phase 2 implements 6 of 7 components (80% coverage): C (Current Earnings), A (Annual Growth), N (Newness/New Highs), S (Supply/Demand), I (Institutional Sponsorship), M (Market Direction).
    • Composite scoring (0-100) with weighted components: C 19%, A 25%, N 19%, S 19%, I 13%, M 6% (renormalized for 6 components).
    • NEW: Volume-based accumulation/distribution analysis (S component) - detects institutional buying patterns via up-day vs down-day volume ratios.
    • NEW: Institutional ownership tracking (I component) - analyzes holder count + ownership % with automatic Finviz fallback when FMP data incomplete.
    • Finviz integration: Free web scraping for institutional data (beautifulsoup4), improves I component accuracy from 35/100 to 60-100/100.
    • Interpretation bands: Exceptional+ (90-100), Exceptional (80-89), Strong (70-79), Above Average (60-69).
    • Bear market protection: M component gates all buy recommendations (M=0 triggers "raise cash" warning).
    • FMP API + Finviz integration: Free tier sufficient for 40 stocks (~1 minute 40 seconds execution time).
    • Comprehensive knowledge base: O'Neil's methodology (now includes S and I), scoring formulas, interpretation guide, portfolio construction rules.
    • Future Phase 3 will add L (Leadership/RS Rank) component for full 7-component CANSLIM (100% coverage).

  • Value Dividend Screener (value-dividend-screener)

    • Screens US stocks for high-quality dividend opportunities using Financial Modeling Prep (FMP) API.
    • Multi-phase filtering: Value characteristics (P/E ≤20, P/B ≤2) + Income (Yield ≥3.5%) + Growth (3-year dividend/revenue/EPS uptrends).
    • Advanced analysis: Dividend sustainability (payout ratios, FCF coverage), financial health (D/E, liquidity), quality scores (ROE, margins).
    • Composite scoring system ranks stocks by overall attractiveness balancing value, growth, and quality factors.
    • Generates top 20 ranked stocks with detailed fundamental analysis and portfolio construction guidance.
    • Includes comprehensive screening methodology documentation and FMP API usage guide.

  • Dividend Growth Pullback Screener (dividend-growth-pullback-screener)

    • Finds high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) experiencing temporary pullbacks.
    • Combines fundamental dividend analysis with technical timing indicators (RSI ≤40 oversold conditions).
    • Targets stocks with exceptional dividend growth rates that compound wealth through dividend increases rather than high current yield.
    • Two-stage screening approach: FINVIZ Elite for fast RSI pre-screening + FMP API for detailed fundamental analysis.
    • Optimized for long-term dividend growth investors seeking entry opportunities during short-term market weakness.
    • Generates ranked lists of quality dividend growers at attractive technical entry points.

  • Kanchi Dividend SOP (kanchi-dividend-sop)

    • Converts Kanchi-style 5-step dividend investing into a repeatable US-stock workflow.
    • Covers screening, deep-dive quality checks, valuation mapping, one-off profit filters, and pullback entry planning.
    • Includes reusable defaults for safety thresholds, valuation interpretation, and one-page stock memo output.
    • Designed as the first step in the Kanchi dividend workflow stack.

  • Kanchi Dividend Review Monitor (kanchi-dividend-review-monitor)

    • Implements forced-review anomaly detection for T1-T5 triggers with deterministic OK/WARN/REVIEW outputs.
    • Focuses on alerting and review-ticket generation, never auto-selling.
    • Includes a local rule-engine script (build_review_queue.py) and unit tests for trigger boundaries.
    • Designed as the ongoing monitoring layer after candidate selection.

  • Kanchi Dividend US Tax Accounting (kanchi-dividend-us-tax-accounting)

    • Provides US dividend tax classification and account-location workflow for income portfolios.
    • Covers qualified vs ordinary assumptions, holding-period checks, and account placement tradeoffs.
    • Includes templates for annual planning memos and unresolved tax-assumption tracking.
    • Designed as the portfolio-implementation layer after screening and monitoring.

  • Pair Trade Screener (pair-trade-screener)

    • Statistical arbitrage tool for identifying and analyzing pair trading opportunities using cointegration testing.
    • Tests for long-term equilibrium relationships between stock pairs within same sector or industry.
    • Calculates hedge ratios, mean-reversion speed (half-life), and generates z-score-based entry/exit signals.
    • Market-neutral strategy profiting from relative price movements regardless of overall market direction.
    • Supports sector-wide screening and custom pair analysis with statistical rigor (ADF tests, correlation analysis).
    • FMP API integration with JSON output for structured results and further analysis.

  • FinViz Screener (finviz-screener)

    • Translates natural-language stock screening requests (Japanese/English) into FinViz screener filter codes and opens the results in Chrome.
    • Supports 500+ filter codes across fundamentals (P/E, dividend, growth, margins), technicals (RSI, SMA, patterns), and descriptives (sector, market cap, country).
    • Theme & Sub-theme cross-screening: Combine FinViz's 30+ investment themes and 268 sub-themes with any filter. Screen for cross-sector narratives like "AI × Logistics", "Data Centers × Power Infrastructure", or "Cybersecurity × Cloud" — something traditional sector/industry filters cannot do. Use --themes and --subthemes to mix multiple themes in a single query (e.g., --themes "artificialintelligence,cybersecurity" --filters "cap_midover").
    • Auto-detects FINVIZ Elite from $FINVIZ_API_KEY environment variable; falls back to public screener when not set.
    • Includes 14 pre-built screening recipes (high dividend value, small-cap growth, oversold large-caps, breakout candidates, AI/theme investing, etc.).
    • No API key required for basic use (public FinViz screener). FINVIZ Elite optional for enhanced features.

Workflow Examples

Daily Market Monitoring

  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

Weekly Strategy Review

  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

Individual Stock Research

  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

Strategic Positioning

  1. Use Stanley Druckenmiller Investment Advisor 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

Earnings Momentum Trading

  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

Income Portfolio Construction

  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

Kanchi Dividend Workflow (US Stocks)

  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

Options Strategy Development

  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.)

Portfolio Review & Rebalancing

  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. Evaluate position-level recommendations (HOLD/ADD/TRIM/SELL) based on thesis validation
  4. Use Market Environment Analysis and US Market Bubble Detector to assess macro conditions
  5. Execute rebalancing plan with specific buy/sell actions to optimize allocation

Statistical Arbitrage Opportunities

  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

Skill Quality & Automation

  • 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.

Skill Self-Improvement Loop

An automated pipeline that continuously reviews and improves skill quality. A daily launchd job picks one skill, scores it with the dual-axis reviewer, and if the score is below 90/100, invokes claude -p to apply improvements and open a PR.

How It Works

  1. Round-robin selection — cycles through all skills (excluding the reviewer itself), persisted in logs/.skill_improvement_state.json.
  2. Auto scoring — runs run_dual_axis_review.py to get a deterministic score (0-100).
  3. Improvement gate — if auto_review.score < 90, Claude CLI applies fixes to SKILL.md and references.
  4. Quality gate — re-scores after improvement (with tests enabled); rolls back if the score didn't improve.
  5. PR creation — commits changes to a feature branch and opens a GitHub PR for human review.
  6. Daily summary — writes results to reports/skill-improvement-log/YYYY-MM-DD_summary.md.

Manual Execution

# Dry-run: score one skill without applying improvements or creating PRs
python3 scripts/run_skill_improvement_loop.py --dry-run

# Review all skills in dry-run mode
python3 scripts/run_skill_improvement_loop.py --dry-run --all

# Full run: score, improve if needed, and open PR
python3 scripts/run_skill_improvement_loop.py

launchd Setup (macOS)

The loop runs daily at 05:00 local time via macOS launchd:

# Install the agent
cp launchd/com.trade-analysis.skill-improvement.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.trade-analysis.skill-improvement.plist

# Verify
launchctl list | grep skill-improvement

# Manual trigger
launchctl start com.trade-analysis.skill-improvement

Key Files

File Purpose
scripts/run_skill_improvement_loop.py Orchestration script (selection, scoring, improvement, PR)
scripts/run_skill_improvement.sh Thin shell wrapper for launchd
launchd/com.trade-analysis.skill-improvement.plist macOS launchd agent configuration
skills/dual-axis-skill-reviewer/ Reviewer skill (scoring engine)
logs/.skill_improvement_state.json Round-robin state and history
reports/skill-improvement-log/ Daily summary reports

Skill Auto-Generation Pipeline

An automated pipeline that mines session logs for skill ideas (weekly) and designs, reviews, and creates new skills as PRs (daily). Works alongside the Self-Improvement Loop to continuously expand the skill catalog.

How It Works

  1. Weekly mining — scans Claude Code session logs for recurring patterns that could become skills, scores each idea for novelty, feasibility, and trading value.
  2. Backlog scoring — ranked ideas are stored in logs/.skill_generation_backlog.yaml with status tracking (pending, in_progress, completed, design_failed, review_failed, pr_failed).
  3. Daily selection — picks the highest-scoring pending idea; retries design_failed / pr_failed once (but review_failed is terminal).
  4. Design & review — the Skill Designer builds a complete skill (SKILL.md, references, scripts), then the Dual-Axis Reviewer scores it. If the score is too low, the idea is marked review_failed.
  5. PR creation — commits the new skill to a feature branch and opens a GitHub PR for human review.

Manual Execution

# Weekly: mine ideas from session logs and score them
python3 scripts/run_skill_generation_pipeline.py --mode weekly --dry-run

# Daily: design a skill from the highest-scoring backlog idea
python3 scripts/run_skill_generation_pipeline.py --mode daily --dry-run

# Full daily run (creates branch, designs skill, opens PR)
python3 scripts/run_skill_generation_pipeline.py --mode daily

launchd Setup (macOS)

Two launchd agents handle the weekly and daily schedules:

# Install both agents
cp launchd/com.trade-analysis.skill-generation-weekly.plist ~/Library/LaunchAgents/
cp launchd/com.trade-analysis.skill-generation-daily.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.trade-analysis.skill-generation-weekly.plist
launchctl load ~/Library/LaunchAgents/com.trade-analysis.skill-generation-daily.plist

# Verify
launchctl list | grep skill-generation

# Manual trigger
launchctl start com.trade-analysis.skill-generation-weekly
launchctl start com.trade-analysis.skill-generation-daily

Key Files

File Purpose
scripts/run_skill_generation_pipeline.py Orchestration script (mining, selection, design, review, PR)
scripts/run_skill_generation.sh Thin shell wrapper for launchd
launchd/com.trade-analysis.skill-generation-weekly.plist Weekly mining schedule (Saturday 06:00)
launchd/com.trade-analysis.skill-generation-daily.plist Daily generation schedule (07:00)
skills/skill-idea-miner/ Mining and scoring skill
skills/skill-designer/ Skill design prompt builder
logs/.skill_generation_backlog.yaml Scored idea backlog with status tracking
logs/.skill_generation_state.json Run history and state
reports/skill-generation-log/ Daily generation summary reports

Customization & Contribution

  • 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.

API Requirements

Several skills require API keys for data access:

Skills Requiring APIs

Skill FMP API FINVIZ Elite Alpaca Notes
Economic Calendar Fetcher ✅ Required ❌ Not used ❌ Not used Fetches economic events
Earnings Calendar ✅ Required ❌ Not used ❌ Not used Fetches earnings dates
Institutional Flow Tracker ✅ Required ❌ Not used ❌ Not used 13F filings analysis, free tier sufficient
Value Dividend Screener ✅ Required 🟡 Optional ❌ Not used FINVIZ reduces execution time 70-80%
Dividend Growth Pullback Screener ✅ Required 🟡 Optional ❌ Not used FINVIZ for RSI pre-screening
Kanchi Dividend SOP ❌ Not used ❌ Not used ❌ Not used Knowledge workflow; uses outputs from other skills or manual lists
Kanchi Dividend Review Monitor ❌ Not used ❌ Not used ❌ Not used Local rule engine; consumes normalized input JSON
Kanchi Dividend US Tax Accounting ❌ Not used ❌ Not used ❌ Not used Knowledge workflow for classification/account location
Pair Trade Screener ✅ Required ❌ Not used ❌ Not used Statistical arbitrage analysis
Options Strategy Advisor 🟡 Optional ❌ Not used ❌ Not used FMP for stock data; theoretical pricing works without
Portfolio Manager ❌ Not used ❌ Not used ✅ Required Real-time holdings via Alpaca MCP
CANSLIM Stock Screener ✅ Required ❌ Not used ❌ Not used Phase 2 (6 components); free tier sufficient; Finviz web scraping for institutional data
VCP Screener ✅ Required ❌ Not used ❌ Not used Stage 2 + VCP pattern screening; free tier sufficient
FTD Detector ✅ Required ❌ Not used ❌ Not used Index price data for rally/FTD detection
Macro Regime Detector ✅ Required ❌ Not used ❌ Not used Cross-asset ETF ratio analysis
Market Breadth Analyzer ❌ Not used ❌ Not used ❌ Not used Uses free GitHub CSV data
Uptrend Analyzer ❌ Not used ❌ Not used ❌ Not used Uses free GitHub CSV data
Sector Analyst ❌ Not used ❌ Not used ❌ Not used Uses free GitHub CSV data; optional chart images
Theme Detector 🟡 Optional 🟡 Optional ❌ Not used Core: FINVIZ public + yfinance (free). FMP for ETF holdings, FINVIZ Elite for stock lists
FinViz Screener ❌ Not used 🟡 Optional ❌ Not used Public screener free; FINVIZ Elite auto-detected from $FINVIZ_API_KEY
Edge Candidate Agent ❌ Not used ❌ Not used ❌ Not used Local YAML generation; validates against local pipeline repo
Trade Hypothesis Ideator ❌ Not used ❌ Not used ❌ Not used Local JSON hypothesis pipeline with optional strategy export
Edge Strategy Reviewer ❌ Not used ❌ Not used ❌ Not used Deterministic scoring on local YAML drafts
Edge Pipeline Orchestrator ❌ Not used ❌ Not used ❌ Not used Orchestrates local edge skills via subprocess
Edge Signal Aggregator ❌ Not used ❌ Not used ❌ Not used Aggregates local edge-skill JSON/YAML outputs into weighted ranked signals
Trader Memory Core 🟡 Optional ❌ Not used ❌ Not used FMP only for MAE/MFE in postmortem; core features work offline
Exposure Coach 🟡 Optional ❌ Not used ❌ Not used FMP only when institutional-flow-tracker data is included
Signal Postmortem 🟡 Optional ❌ Not used ❌ Not used FMP for fetching realized returns; manual price entry also supported
Dual-Axis Skill Reviewer ❌ Not used ❌ Not used ❌ Not used Deterministic scoring + optional LLM review

API Setup

Financial Modeling Prep (FMP) API:

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

Support & Further Reading

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.

License

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

About

Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development.

tradermonty.github.io/claude-trading-skills/

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