Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/leecyno1/boutique-skillsnpx agentmods add skills/leecyno1/boutique-skills/canslim-screenerWrote 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/canslim-screener)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/canslim-screener"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/canslim-screener/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/canslim-screener"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/canslim-screener.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 | $0.00060 | $0.06829 |
| Opus 5 | $0.00030 | $0.03415 |
| Sonnet 5 | $0.00012 | $0.01366 |
| Haiku 4.5 | $0.00006 | $0.00683 |
Grade A, and why
canslim-screener 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 8d 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 canslim-screener — 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 — 672 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CANSLIM Stock Screener - Phase 3 (Full CANSLIM)
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: Current Earnings, Annual Growth, Newness/New Highs, Supply/Demand, Leadership/RS Rank, Institutional Sponsorship, and Market Direction.
Phase 3 implements all 7 of 7 components (C, A, N, S, L, I, M), representing 100% of the full methodology.
Two-Stage Approach:
- Stage 1 (FMP API + Finviz): Analyze stock universe with all 7 CANSLIM components
- 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 protection (M component gating)
Phase 3.1 Component Weights (Original O'Neil weights):
- C (Current Earnings): 15%
- A (Annual Growth): 20%
- N (Newness): 15%
- S (Supply/Demand): 15%
- L (Leadership/RS Rank): 20% — multi-period weighted RS (3m/6m/12m vs configurable benchmark)
- I (Institutional): 10%
- M (Market Direction): 5%
Weighted RS Formula:
Weighted RS = 0.40 × rel_3m + 0.30 × rel_6m + 0.30 × rel_12m
Available periods are re-normalized when some are missing. Default benchmark is ^GSPC;
override with --rs-benchmark SPY/QQQ/IWM/....
Fallback hierarchy when multi-period data is incomplete:
- No benchmark → weighted absolute stock performance + 20% penalty.
- All multi-period windows missing but >=50 bars of price history → fall back to the legacy 365-day full-window absolute return as the scoring input (20% penalty if no benchmark).
- <50 bars of price history → score=0 with
errorset.
What ships with it
23 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.
- references/canslim_methodology.md 25 KB
- references/fmp_api_endpoints.md 19 KB
- references/interpretation_guide.md 17 KB
- references/scoring_system.md 20 KB
- scripts/calculators/earnings_calculator.py 14 KB runs code
- scripts/calculators/growth_calculator.py 11 KB runs code
- scripts/calculators/institutional_calculator.py 15 KB runs code
- scripts/calculators/leadership_calculator.py 24 KB runs code
- scripts/calculators/market_calculator.py 8.2 KB runs code
- scripts/calculators/new_highs_calculator.py 6.3 KB runs code
- scripts/calculators/supply_demand_calculator.py 7.8 KB runs code
- scripts/check_institutional_endpoint.py 3.0 KB runs code
- scripts/finviz_stock_client.py 7.0 KB runs code
- scripts/fmp_client.py 22 KB runs code
- scripts/report_generator.py 19 KB runs code
- scripts/scorer.py 21 KB runs code
- scripts/screen_canslim.py 19 KB runs code
- scripts/tests/conftest.py 412 B runs code
- scripts/tests/test_canslim_fixes.py 21 KB runs code
- scripts/tests/test_fmp_fallback.py 17 KB runs code
- scripts/tests/test_fmp_stable_migration.py 5.2 KB runs code
- scripts/tests/test_institutional_fallback.py 4.4 KB runs code
- scripts/tests/test_leadership_rs.py 20 KB runs code
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.
- 8d ago First seen · 672 lines · 60 tokens per session scan A e2450caba733
canslim-screener is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 6,829 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to canslim-screener, 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.