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Trader Screener

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Jakeschincariol
trader-screener

The Stock Screener. Checks a list of companies against the user's own rules (revenue growth, margins, EPS, P/E, earnings yield, sector, anything in the metrics file), says exactly which rule each company passes or fails and by how much, and ranks the ones that pass into a shortlist. Reads the metrics /trader-analyst writes from SEC filings, or any CSV. Use when the user says "screen for stocks", "find me companies that", "filter my watchlist", "which of these pass my rules", "shortlist candidates", "rank these stocks", or "growing companies under 30 times earnings".

Overview

PublisherJakeschincariol
Repositorytrader-skill
Skill nametrader-screener
Stars
406
Forks
67
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Jakeschincariol on GitHub. Read the source before you install it.

Installation

Install the Trader Screener AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Jakeschincariol/trader-skill/tree/main/trader-screener
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Jakeschincariol/trader-skill.git /tmp/trader-skill
mkdir -p .claude/skills
cp -r /tmp/trader-skill/trader-screener .claude/skills/trader-screener
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trader Screener in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Trader Screener on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Trader Screener is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

trader-screener

A screen turns "I want profitable companies that are still growing and not too expensive" into rules a computer can check, and shows its work: every pass and every fail comes with the number and the threshold.

Tool in this folder (standard library Python, no network):

bash
python3 "${CLAUDE_SKILL_DIR}/screen.py" --help

What it reads and writes

  • Metrics: trader/metrics.json from /trader-analyst (facts.py --metrics), or any JSON list or CSV with a ticker column. CSV numbers may carry $, % and commas.
  • Prices (optional): trader/prices.json ({"prices": {"AAPL": 341.27}}, any source; /trader-alerts writes it). With prices it adds price, pe (price / trailing EPS, only when EPS is positive) and earnings_yield.
  • Rules: a JSON file, rules.example.json in this folder to start from.
  • Writes trader/shortlist.json with --out: who passed, their score and why, who failed and why.

The rules file

json
{"name": "Growing, profitable, not priced for perfection",
 "rules": [{"metric": "revenue_growth_yoy", "op": ">=", "value": 10},
           {"metric": "operating_margin", "op": ">=", "value": 25},
           {"metric": "pe", "op": "<=", "value": 35}],
 "rank":  [{"metric": "revenue_growth_yoy", "order": "desc", "weight": 2},
           {"metric": "pe", "order": "asc", "weight": 1}],
 "missing": "fail"}

Ops: >, >=, <, <=, ==, !=, between ([low, high]), in and not in (a list, for tickers or sectors to leave out). "missing": "fail" fails a company that lacks a metric a rule needs (banks have no operating margin in SEC's facts); "skip" ignores that rule for it and says so. Ranking scores each passing company 0 to 1 per rank metric and takes the weighted average.

Steps

  1. Turn the user's words into rules. Ask when a threshold is vague ("cheap" means what P/E?). Write them to trader/rules.json.

  2. Get metrics for the candidates: run /trader-analyst on them with --metrics trader/metrics.json (its facts.py AAPL AXP GOOGL KO BAC --metrics trader/metrics.json), or use the user's own CSV. Prices from trader/prices.json if P/E matters.

  3. Run the screen:

    bash
    python3 "${CLAUDE_SKILL_DIR}/screen.py" trader/metrics.json --rules trader/rules.json \
      --prices trader/prices.json --out trader/shortlist.json

    Exit code 1 means nothing passed: say so and show the closest misses.

  4. Report the shortlist with the reasons, the misses with the numbers that failed, and the dates the numbers are from (the latest quarter end per company, the price date). Suggest /trader-analyst on the top names before anything else.

Example: the reel's five

Metrics from SEC for AAPL, AXP, GOOGL, KO and BAC, prices at the 2026-10-13 close, and rules.example.json:

Growing, profitable, not priced for perfection: 2 of 5 companies pass.
 1. PASS GOOGL  Alphabet Inc.  score 0.75   (growth 24.2, P/E 17.8)
 2. PASS KO     COCA COLA CO   score 0.25   (growth 12.1, P/E 28.3)
    FAIL AAPL   pe 39.2, needs <= 35
    FAIL BAC    operating_margin is missing
    FAIL AXP    revenue_growth_yoy 8.6, needs >= 10; operating_margin is missing

Then the caveats a careful analyst adds: Alphabet's latest quarter has net income above operating income (gains outside the business), and Coca-Cola's numbers stop at the quarter ending 2026-04-03 because SEC's facts lag its latest 10-Q.

What it never does

  • Never calls a passing company a buy. A screen is a filter on reported numbers, not a recommendation.
  • Never fills in a missing number or guesses a metric: missing means missing.
  • Never sends anything to /trader-budget or an order path by itself.

Safety

Education and tooling, not financial advice. This skill only filters numbers. Nothing places an order except /trader-broker, after /trader-risk passes and the user types an explicit yes for that order.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Trader Screener AI skill do?

The Stock Screener. Checks a list of companies against the user's own rules (revenue growth, margins, EPS, P/E, earnings yield, sector, anything in the metrics file), says exactly which rule each company passes or fails and by how much, and ranks the ones that pass into a shortlist. Reads the metrics /trader-analyst writes from SEC filings, or any CSV. Use when the user says "screen for stocks", "find me companies that", "filter my watchlist", "which of these pass my rules", "shortlist candidates", "rank these stocks", or "growing companies under 30 times earnings".

Why use Trader Screener on TypingMind?

Because you install it once and use it with any model. Trader Screener is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Trader Screener in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jakeschincariol/trader-skill/tree/main/trader-screener. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Trader Screener?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Trader Screener?

As many as you like. As long as a model supports skills, you can use Trader Screener with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Trader Screener AI skill free?

Yes. It is published on GitHub by Jakeschincariol under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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