canslim-screener
Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
npx skills add majiayu000/claude-skill-registry --skill canslim-screener-nicepkg-ai-workflow-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Screens US stocks using William O'Neil's CANSLIM methodology (phase 2) focusing on 6 components (C, A, N, S, I, M) to rank stocks and generate actionable reports.
How it works
- Stage 1: Analyze a stock universe via API data and web scraping to compute the 6 CANSLIM components: C (Current Earnings), A (Annual Growth), N (Newness/New Highs), S (Supply/Demand), I (Institutional), M (Market Direction).
- Step 1 checks for a valid FMP API key and required Python environment libraries.
- Step 2 determines the stock universe (default top 40 S&P 500 by market cap) or a custom sector/universe.
- Step 3 runs the main screening script which:
- Evaluates M to assess market trend and bear market warnings.
- Calculates C, A, N, S, I, M per stock; applies phase-2 weights (C 19%, A 25%, N 19%, S 19%, I 13%, M 6%).
- Ranks stocks by composite score and generates JSON + Markdown outputs.
- Step 4 outputs include a latest report markdown with market summary, top CANSLIM candidates, component breakdowns (including S and I), and data notes (Finviz fallback when needed).
- Step 5 provides user-facing recommendations and risk notes, plus next steps.
When to use it
- Trigger phrases: "Find CANSLIM stocks", "Screen for growth stocks using O'Neil's method", "Which stocks have strong earnings and momentum?" and related prompts.
- Use when user wants systematic stock selection based on CANSLIM with growth and momentum focus.
- Note bear markets: if M=0, the system warns to raise cash and not buy.
What it can touch
- Scripts and endpoints: uses FMP API (get_income_statement, get_quote, get_historical_prices, get_institutional_holders) and Finviz web scraping to obtain institutional ownership when needed. It documents rate limits and fallback behavior.
- Components touch: C, A, N, S, I, M computations; composite scoring; and report generation (JSON and Markdown).
Caveats
- Phase 2 covers 6 of 7 CANSLIM components; L (Leadership/RS Rank) is not yet included.
- Finviz institutional data is used as a fallback when FMP data is incomplete, which may affect I component accuracy.
- Bear market override can prevent purchases even with high scores.
- Requires API key and network access; rate limits and free-tier constraints are noted (e.g., 250 calls/day, 40-stock screening estimates).
# CANSLIM Stock Screener - Phase 2 ## Overview This skill screens US stocks using William O'Neil's proven CANSLIM methodology, a systematic approach for identifying growth stocks with strong fundamentals and price momentum. CANSLIM analyzes 7 key components: **C**urrent Earnings, **A**nnual Growth, **N**ewness/New Highs, **S**upply/Demand, **L**eadership/RS Rank, **I**nstitutional Sponsorship, and **M**arket Direction. **Phase 2** implements 6 of 7 components (C, A, N, S, I, M), representing **80% of the full methodology**. This phase adds critical volume analysis (S) and institutional ownership tracking (I) to the Phase 1 foundation. **Two-Stage Approach:** 1. **Stage 1 (FMP API + Finviz)**: Analyze stock universe with 6 CANSLIM components 2. **Stage 2 (Reporting)**: Rank by composite score and generate actionable reports **Key Features:** - Composite scoring (0-100 scale) with weighted components - **Finviz fallback** for institutional ownership data (automatic when FMP data incomplete) - Progressive filtering to optimize API usage - JSON + Markdown output formats - Interpretation bands: Exceptional+ (90+), Exceptional (80-89), Strong (70-79), Above Average (60-69) - Bear market
- Overview
- When to Use This Skill
- Workflow
- Step 1: Verify API Access and Requirements
- Step 2: Determine Stock Universe
- Step 3: Execute CANSLIM Screening Script
- Step 4: Read and Parse Screening Results
- Step 5: Analyze Top Candidates and Provide Recommendations
- Step 6: Generate User-Facing Report
- Resources
- Scripts Directory (scripts/)
- References Directory (references/)
- Troubleshooting
- Issue 1: FMP API Rate Limit Exceeded
Check environment variable echo $FMP_API_KEY If not set, prompt user to provide it pip install requests beautifulsoup4 lxml python3 skills/canslim-screener/scripts/screen_canslim.py python3 skills/canslim-screener/scripts/screen_canslim.py \ cd /Users/takueisaotome/PycharmProjects/claude-trading-skills/skills/canslim-screener/scripts Basic run (40 stocks, top 20 in report) python3 screen_canslim.py --api-key $FMP_API_KEY Custom parameters
What does the canslim-screener skill do?
Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill canslim-screener-nicepkg-ai-workflow-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.