oneil-canslim

oneil-canslim is a skill for Claude Code, Codex from questflowai/investorskills. It costs 38 tokens per session (564 once invoked), scanned A, original, MIT.

A stock-analysis method based on William O’Neil’s CAN SLIM approach, which looks for strong earnings and sales, market leadership, investor demand, and favorable chart patterns. It also considers the overall market direction and disciplined exit levels.

In plain words
What is it for?
Use it to screen growth stocks, review earnings and sales trends, compare relative strength, assess patterns such as cup-with-handle bases, and plan breakout trades.
Why use it?
It provides a defined checklist for judging growth stocks instead of relying on a single metric or intuition. It helps filter out weak businesses, poor market conditions, and overly extended price moves.

Skill for Claude CodeCodex

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

Good fit Use it to screen growth stocks, review earnings and sales trends, compare relative strength, assess patterns such as cup-with-handle bases, and plan breakout trades.

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Install with agentmods
npx agentmods add skills/questflowai/investorskills/oneil-canslim
About the project

Investor Skills is an open-source library that organizes investing judgment—such as evaluating opportunities, managing risk, and acting under uncertainty—into structured, reusable instructions for people and AI finance agents. It is designed for studying and applying investment approaches, including inside Questflow and other agent tools. The catalogue includes portable skill packages from the library.

questflowai/investorskills · 1,849 stars · on GitHub · questflow.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add questflowai/investorskills --skill oneil-canslim
Clone the repo
git clone --depth 1 https://github.com/questflowai/investorskills

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 oneil-canslim

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/questflowai/investorskills/oneil-canslim"><img src="https://agentmods.dev/badge/skills/questflowai/investorskills/oneil-canslim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 564 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00038 $0.00564
Opus 5 $0.00019 $0.00282
Sonnet 5 $0.00008 $0.00113
Haiku 4.5 $0.00004 $0.00056

Measured 11d ago against content hash 53311fcca9c1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

oneil-canslim 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 11d 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.

skills/oneil-canslim/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

O'Neil CAN SLIM

Use this skill to apply William O'Neil-style growth stock selection: find companies with accelerating earnings and sales, strong relative strength, constructive bases, institutional sponsorship, and a favorable general market.

When To Use

Use this skill when the user asks for:

  • CAN SLIM analysis
  • Growth stock screening
  • Relative strength and earnings acceleration checks
  • Cup-with-handle, flat base, or base breakout review
  • A 7-8% stop-loss plan for a growth stock trade

Trigger phrases include CAN SLIM, O'Neil, relative strength, base breakout, cup with handle, IBD, earnings acceleration, and 7% stop.

Do Not Use When

  • The company has weak or decelerating fundamentals.
  • The market is in a confirmed downtrend.
  • The stock is thinly traded or extended far above a proper buy point.
  • The user wants deep value or long-term moat analysis only.

Inputs Needed

  • Ticker and exchange
  • Quarterly EPS and sales growth
  • Annual EPS trend
  • Relative strength vs. market and peers
  • Base pattern, pivot price, volume, and current price
  • General market trend

Process

  1. Check CAN SLIM fundamentals first: current and annual earnings, sales, new product/catalyst.
  2. Confirm leadership: relative strength, industry group strength, and price near new highs.
  3. Evaluate supply and demand: float, volume dry-up, accumulation, institutional sponsorship.
  4. Confirm market direction.
  5. Judge base quality and pivot validity.
  6. Define buy point, stop, add rules, and sell rules.

Output Format

# CAN SLIM View: [Stock]

## Verdict
Buy Point / Watchlist / Extended / Pass / Market Stand Down

## CAN SLIM Checklist
| Letter | Status | Evidence |
|--------|--------|----------|

## Base And Pivot

## Buy / Stop Plan

## Sell Rules

## Missing Data

Guardrails

  • Do not buy cheap-looking laggards.
  • Do not buy extended breakouts.
  • Do not ignore the general market trend.
  • Cut losses quickly, usually around 7-8% from the proper buy point.
  • Do not average down.

Read the full file on GitHub · 80 lines

Files

What ships with it

1 file 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.

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. 11d ago First seen · 80 lines · 38 tokens per session scan A 53311fcca9c1

Subscribe to this mod's changes

oneil-canslim is a skill published in the GitHub repository questflowai/investorskills (1,849 stars, last pushed 19d ago), licensed MIT. It adds 38 tokens to every session and 564 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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