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Document Code

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codeaholicguy
document-code

AI DevKit · Document a code entry point with structured analysis, dependency mapping, and saved knowledge docs. Use when users ask to document, understand, or map code for a module, file, folder, function, or API.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namedocument-code
Stars
1.6K
Forks
252
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Document Code 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.
  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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/document-code .claude/skills/document-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Document Code 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 Document Code 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 Document Code 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.

Code Documentation Assistant

Build structured understanding of code entry points with an analysis-first workflow.

Hard Rule

  • Do not create documentation until the entry point is validated and analysis is complete.

Workflow

  1. Gather & Validate
  • Confirm entry point (file, folder, function, API), purpose, and desired depth.
  • Verify it exists; resolve ambiguity or suggest alternatives if not found.
  • Search for existing knowledge before analyzing: npx ai-devkit@latest memory search --query "<entry point name or purpose>"
  1. Collect Source Context
  • Summarize purpose, exports, key patterns.
  • Folders: list structure, highlight key modules.
  • Functions/APIs: capture signature, parameters, return values, error handling.
  1. Analyze Dependencies
  • Build dependency view up to depth 3, track visited nodes to avoid loops.
  • Categorize: imports, function calls, services, external packages.
  • Exclude external systems or generated code.
  1. Synthesize
  • Overview (purpose, language, high-level behavior).
  • Core logic, execution flow, patterns.
  • Error handling, performance, security considerations.
  • Improvements or risks discovered during analysis.
  1. Create Documentation
  • Normalize name to kebab-case (calculateTotalPricecalculate-total-price).
  • Create docs/ai/implementation/knowledge-{name}.md using the Output Template — this is the source of truth.
  • Include mermaid diagrams when they clarify flows or relationships.
  1. Offer HTML Artifact
  • After the markdown is written, ask the user once: "Also generate an HTML artifact for easier scanning? (y/N)".
  • If yes, generate sibling docs/ai/implementation/knowledge-{name}.html per the HTML Artifact spec. Regenerate from the markdown on subsequent runs; never hand-edit.
  • If no or no response, stop here — markdown alone is a complete result.

HTML Artifact

Generated only when the user opts in at step 6. A self-contained HTML file optimized for scanning, not reference reading. Complements the markdown — does not replace it.

Constraints:

  • Single file. Inline CSS. No build step. Only external asset allowed is mermaid via CDN (https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js).
  • Card-based grid layout, not a long scroll. The reader should capture structure at a glance.
  • Responsive down to laptop width. Print-friendly.
  • No interactivity beyond collapsible deep-dives and mermaid pan/zoom.

Section mapping (from the Output Template):

  • Overview → hero card: title, one-line purpose, language/type badges.
  • Implementation Details → grid of sectioned cards with short bullets, not prose.
  • Dependencies → graph card (mermaid) plus a categorized list (imports, calls, services, external).
  • Visual Diagrams → full-width rendered mermaid blocks.
  • Additional Insights → callout boxes, color-coded by kind (info, warning, risk).
  • Next Steps → checklist card.
  • Metadata → compact footer (date, depth, files touched).

Red Flags and Rationalizations

RationalizationWhy It's WrongDo Instead
"I already understand this code"Understanding ≠ documented understandingWrite it down, then verify
"The code is self-documenting"Future readers lack your current contextCapture the why, not just the what
"Dependencies are obvious"Implicit dependencies cause surprisesMap them explicitly to depth 3

Validation

  • Documentation covers all Output Template sections.
  • If an HTML artifact was generated, it opens standalone in a browser, renders mermaid, and reflects the markdown content (no drift).
  • Summarize key insights, open questions, and related areas for deeper dives.
  • Confirm file path(s) and remind to commit.

Output Template

  • Overview
  • Implementation Details
  • Dependencies
  • Visual Diagrams (mermaid)
  • Additional Insights
  • Metadata (date, depth, files touched)
  • Next Steps

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 Document Code AI skill do?

AI DevKit · Document a code entry point with structured analysis, dependency mapping, and saved knowledge docs. Use when users ask to document, understand, or map code for a module, file, folder, function, or API.

Why use Document Code on TypingMind?

Because you install it once and use it with any model. Document Code 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 Document Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/document-code. 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 Document Code?

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 Document Code?

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

Is the Document Code AI skill free?

Yes. It is published on GitHub by codeaholicguy under the Apache-2.0 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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