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Generate Docs

Organization
DasDigitaleMomentum
generate-docs

Generate project documentation from an existing codebase. This documentation shall serve Agents and Humans. Creates a project overview, module documentation, and feature documentation with explicit inventories (files/dirs + symbols) for each module. Use this skill when onboarding a new project or creating initial documentation for an undocumented codebase.

Overview

PublisherDasDigitaleMomentum
Repositoryopencode-processing-skills
Skill namegenerate-docs
Stars
61
Forks
3
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Generate Docs 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/DasDigitaleMomentum/opencode-processing-skills/tree/main/skills/generate-docs
  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/DasDigitaleMomentum/opencode-processing-skills.git /tmp/opencode-processing-skills
mkdir -p .claude/skills
cp -r /tmp/opencode-processing-skills/skills/generate-docs .claude/skills/generate-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Generate Docs 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 Generate Docs 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 Generate Docs 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.

Skill: Generate Documentation

What This Skill Does

Creates structured project documentation from an existing codebase. This documentation shall serve Agents and Humans when working in consecutive sessions with the project.

Produces three artifact types:

  1. Project Overview (docs/overview.md) - High-level architecture, module listing, feature listing
  2. Module Documentation (docs/modules/<name>.md) - Overview + exhaustive inventories (files/dirs + symbols)
  3. Feature Documentation (docs/features/<name>.md) - How features work, with implementation references

When to Use

  • When a project has no structured documentation yet
  • When onboarding to an unfamiliar codebase
  • When the user asks to "document this project" or "create documentation"

Do NOT use this skill to update existing documentation - use update-docs instead.

Execution Model

  • The primary agent spawns doc-explorer. doc-explorer explores the repo and writes artifacts under docs/.
  • Rationale: documentation is primarily anchored in the codebase (not in the conversation). Keeping exploration + writing in the same subagent session reduces context loss.
  • For large codebases with multiple modules, doc-explorer self-delegates: it spawns additional doc-explorer instances scoped to individual modules (see Self-Delegation below).
  • The primary agent should keep chat output minimal (paths changed + any open questions).

Self-Delegation

For projects with 3+ modules or modules with 50+ files, doc-explorer SHOULD delegate per-module work to separate doc-explorer instances via the Task tool:

  1. Orchestrator instance: Identifies modules, creates docs/overview.md, spawns per-module instances
  2. Per-module instance: Receives scoped task ("document module X in directory Y"), explores only that module, writes docs/modules/<name>.md
  3. Orchestrator: Collects status, writes cross-cutting feature docs

This prevents token bloat from accumulating the entire codebase analysis in a single context.

Workflow

Step 1: Assess the Project

Gather information using read/glob/grep and git history.

  • Identify the project type, language, framework
  • Find the entry points, main modules, key directories
  • Identify major features from code, tests, or existing README
  • Check if any documentation already exists (skip what exists, or ask user)

Step 2: Identify Modules

A module is a self-contained part of the project. Criteria for module boundaries:

  • Has its own directory/package structure
  • Has a clear responsibility (e.g., "authentication", "API layer", "database")
  • Could theoretically be replaced independently
  • For single-module projects: the entire project is one module

For large codebases, self-delegate per-module analysis to separate doc-explorer instances.

Step 3: Create the Project Overview

Create docs/overview.md following the template structure:

  • Fill in project purpose and architecture description
  • List all identified modules with brief descriptions
  • List key features with brief descriptions
  • Include development setup if discoverable (package.json, Makefile, etc.)
  • Link to module and feature docs (even if not yet created)

Step 4: Create Module Documentation

For each identified module, create docs/modules/<module-name>.md:

  • Overview section (always): Responsibility, dependencies, boundaries
  • Structure: Exhaustive directory/file inventory for the module (each entry has a purpose)
  • Key Symbols: Exhaustive symbol inventory for the module (each entry has a purpose + location)
  • Data Flow: How data moves through this module

For large modules, self-delegate: spawn a separate doc-explorer instance scoped to that module's directory.

Step 5: Create Feature Documentation

For each identified feature, create docs/features/<feature-name>.md:

  • User flow: what the user experiences
  • Technical flow: what happens under the hood
  • Implementation references: which modules and symbols are involved
  • Edge cases and limitations

Feature docs are inherently incomplete - document what is discoverable and note gaps.

Step 6: Verify and Report

  • Ensure all cross-references between documents are valid
  • Present a summary of created documents
  • Flag any gaps or areas that need manual enrichment

Rules

  1. File-based interface: All output goes into docs/ directory files. Do not return documentation as chat messages.
  2. No redundancy: Don't duplicate information between overview and module/feature docs. Use references.
  3. Stack-agnostic: Do not assume any specific language or framework. Discover everything from the codebase.
  4. Inventories are explained: File/dir and symbol listings MUST include a purpose; listings without explanation are not acceptable.
  5. No built-in explore agent: Do NOT use the built-in explore subagent type. Self-delegate to doc-explorer instead.
  6. Self-delegate for scale: For large codebases, spawn additional doc-explorer instances per module via the Task tool.
  7. Create directories: Ensure docs/, docs/modules/, and docs/features/ exist before writing.

Templates

This skill includes normative templates as bundled files. Only read the templates when processing them. Output MUST follow the template headings and frontmatter keys:

  • tpl-project-overview.md - Structure for the project overview
  • tpl-module-documentation.md - Structure for module documentation
  • tpl-feature-documentation.md - Structure for feature documentation

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 Generate Docs AI skill do?

Generate project documentation from an existing codebase. This documentation shall serve Agents and Humans. Creates a project overview, module documentation, and feature documentation with explicit inventories (files/dirs + symbols) for each module. Use this skill when onboarding a new project or creating initial documentation for an undocumented codebase.

Why use Generate Docs on TypingMind?

Because you install it once and use it with any model. Generate Docs 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 Generate Docs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DasDigitaleMomentum/opencode-processing-skills/tree/main/skills/generate-docs. 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 Generate Docs?

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 Generate Docs?

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

Is the Generate Docs AI skill free?

Yes. It is published on GitHub by DasDigitaleMomentum 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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