canslim-screener

canslim-screener is a skill for Claude Code, Codex from BaggaT236/AI-Trading-Skills. It costs 60 tokens per session (6,829 once invoked), scanned A, a copy of canslim-screener, MIT.

A stock screener based on William O'Neil's CANSLIM method for finding US growth companies with strong earnings, price momentum, and other favorable characteristics. CANSLIM is an investment framework covering earnings, annual growth, new highs, supply and demand, leadership, institutional ownership, and overall market direction.

In plain words
What is it for?
Use it to screen and rank US stocks, combine data from the Financial Modeling Prep service and Finviz, calculate a 0–100 score, and produce JSON or Markdown reports.
Why use it?
It replaces a manual review of many companies with a structured seven-part assessment and ranking. It also considers whether the broader market is strong enough for growth-stock strategies.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is --output-dir ../../../.

Good fit Use it to screen and rank US stocks, combine data from the Financial Modeling Prep service and Finviz, calculate a 0–100 score, and produce JSON or Markdown reports.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/BaggaT236/AI-Trading-Skills
agentmods
npx agentmods add skills/baggat236/ai-trading-skills/canslim-screener

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for canslim-screener

README.md
[![agentmods](https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/canslim-screener/github.svg)](https://agentmods.dev/skills/baggat236/ai-trading-skills/canslim-screener)
Your own site
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/canslim-screener"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-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.

agentmods 80×15 button for canslim-screener

Your own site · 80×15
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/canslim-screener"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/canslim-screener.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,829 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 13d ago against content hash e2450caba733, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 13d ago.

The scan reads SKILL.md. This mod also ships 19 executable files (scripts/calculators/earnings_calculator.py, scripts/calculators/growth_calculator.py, scripts/calculators/institutional_calculator.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

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.

skills/canslim-screener/SKILL.md · 672 lines

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:

  1. Stage 1 (FMP API + Finviz): Analyze stock universe with all 7 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 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:

  1. No benchmark → weighted absolute stock performance + 20% penalty.
  2. 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).
  3. <50 bars of price history → score=0 with error set.

Read the full file on GitHub · 672 lines

Changes

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.

  1. 13d ago First seen · 672 lines · 60 tokens per session scan A e2450caba733

Subscribe to this mod's changes

canslim-screener is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), 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.

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