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Trader Analyst

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Jakeschincariol
trader-analyst

The Earnings Analyst. Reads a company's reported numbers straight from SEC EDGAR (XBRL company facts from its 10-Qs and 10-Ks, plus the latest earnings 8-K and its press release) and sums up what changed: revenue, gross, operating and net margins, net income and diluted EPS, quarter by quarter, each against the same quarter a year earlier, with the trailing twelve months. Writes a metrics file the screener reads. Use when the user says "how did Apple's earnings look", "analyze the earnings", "what changed this quarter", "is revenue growing", "check the 10-Q", "compare margins", or "run the analyst on my holdings".

Overview

PublisherJakeschincariol
Repositorytrader-skill
Skill nametrader-analyst
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 Analyst 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-analyst
  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-analyst .claude/skills/trader-analyst
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trader Analyst 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 Analyst 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 Analyst 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-analyst

Earnings season moves stocks, and the press release is written to put the best number first. This skill reads the numbers the company actually filed with the SEC, lines them up quarter by quarter, and says plainly what changed.

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

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

It needs the SEC User-Agent (sec_user_agent in trader/config.json, or SEC_USER_AGENT): "Your Name you@example.com" with the user's real name and email. It refuses to call SEC without one.

What it reads and writes

  • Reads https://data.sec.gov/api/xbrl/companyfacts/CIK##########.json (every number the company has tagged in its filings), the company's submissions list (for the newest 10-Q or 10-K and the newest 8-K with item 2.02, "Results of Operations") and that 8-K's folder to find the press release (the EX-99.1 exhibit).
  • Writes trader/metrics.json with --metrics: one row per company (TTM revenue, net income and diluted EPS, the latest margins and growth) for /trader-screener. metrics.example.json in this folder is a real run for AAPL, AXP, GOOGL, KO and BAC on 2026-10-13.

The rules

  • Filed numbers first. The table comes from XBRL facts the company filed. Use the press release for context (guidance, segment colour), and say which numbers came from where.
  • Compare like with like. Growth is against the same quarter a year earlier. Quarter-on-quarter changes are shown but many businesses are seasonal (Apple's December quarter is always its biggest).
  • Say what is derived. Companies do not file a Q4 10-Q. The fourth quarter is the 10-K year minus the three quarters inside it and is marked "derived"; derived EPS is approximate.
  • Say when the data lags. SEC's facts API can trail a filing. When the newest 10-Q is later than the newest quarter in the facts, the tool prints a NOTE with the link: read the 10-Q itself before summarising.
  • Flag one-offs. When net income is above operating income, gains outside the business (investment marks, tax items) are inside it. The tool says so. Banks and insurers have no gross margin: blank, not zero.

Steps

  1. Run it for the companies in question (or every ticker in trader/targets.json):

    bash
    python3 "${CLAUDE_SKILL_DIR}/facts.py" AAPL
    python3 "${CLAUDE_SKILL_DIR}/facts.py" AAPL AXP GOOGL KO BAC --metrics trader/metrics.json

    --quarters 8 shows two years, --json everything machine-readable, --cik a company without a ticker.

  2. Open the latest earnings press release (the EX-99 link) and the 10-Q when the numbers need context, and read them.

  3. Write the summary: one line on the quarter (revenue, EPS, growth), one on margins, one on anything unusual (a one-off, a derived quarter, data that lags), and the links. Plain words, real numbers, no adjectives the numbers do not support.

Example: the reel's top holding

facts.py AAPL (2026-10-13): quarter ending 2026-06-27 (fiscal Q3 2026), revenue $109.42B, +16.4% against a year earlier, net income $29.79B (+27.1%), diluted EPS $2.02 (+28.7%); gross margin 50.1% (+0.8 points on the quarter before); last four quarters $466.8B revenue and $8.71 EPS. Press release: the EX-99.1 of the 8-K filed 2026-07-30. For Coca-Cola the tool notes that SEC's facts stop at 2026-04-03 while the 10-Q for 2026-07-03 is already filed, so the summary reads that 10-Q directly.

What it never does

  • Never says a stock is cheap, expensive, a buy or a sell. It reports what the company filed.
  • Never invents a number the filings do not have, or estimates a quarter it cannot derive.
  • Never calls SEC without the User-Agent, and stays well under 10 requests a second.

Safety

Education and tooling, not financial advice. This skill only reads filings. 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 Analyst AI skill do?

The Earnings Analyst. Reads a company's reported numbers straight from SEC EDGAR (XBRL company facts from its 10-Qs and 10-Ks, plus the latest earnings 8-K and its press release) and sums up what changed: revenue, gross, operating and net margins, net income and diluted EPS, quarter by quarter, each against the same quarter a year earlier, with the trailing twelve months. Writes a metrics file the screener reads. Use when the user says "how did Apple's earnings look", "analyze the earnings", "what changed this quarter", "is revenue growing", "check the 10-Q", "compare margins", or "run the...

Why use Trader Analyst on TypingMind?

Because you install it once and use it with any model. Trader Analyst 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 Analyst in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jakeschincariol/trader-skill/tree/main/trader-analyst. 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 Analyst?

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 Analyst?

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

Is the Trader Analyst 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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