Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsnpx agentmods add commands/engineerwithai/engineerwith-agents/doc-generateWrote 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.
[](https://agentmods.dev/commands/engineerwithai/engineerwith-agents/doc-generate)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/doc-generate"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/doc-generate.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03666 |
| Opus 5 | $0.00000 | $0.01833 |
| Sonnet 5 | $0.00000 | $0.00733 |
| Haiku 4.5 | $0.00000 | $0.00367 |
Grade A, and why
doc-generate scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
examples['curl'] = f''' This is a copy
100% identical to doc-generate — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 653 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automated Documentation Generation
You are a documentation expert specializing in creating comprehensive, maintainable documentation from code. Generate API docs, architecture diagrams, user guides, and technical references using AI-powered analysis and industry best practices.
Context
The user needs automated documentation generation that extracts information from code, creates clear explanations, and maintains consistency across documentation types. Focus on creating living documentation that stays synchronized with code.
Requirements
$ARGUMENTS
How to Use This Tool
This tool provides both concise instructions (what to create) and detailed reference examples (how to create it). Structure:
- Instructions: High-level guidance and documentation types to generate
- Reference Examples: Complete implementation patterns to adapt and use as templates
Instructions
Generate comprehensive documentation by analyzing the codebase and creating the following artifacts:
1. API Documentation
- Extract endpoint definitions, parameters, and responses from code
- Generate OpenAPI/Swagger specifications
- Create interactive API documentation (Swagger UI, Redoc)
- Include authentication, rate limiting, and error handling details
2. Architecture Documentation
- Create system architecture diagrams (Mermaid, PlantUML)
- Document component relationships and data flows
- Explain service dependencies and communication patterns
- Include scalability and reliability considerations
3. Code Documentation
- Generate inline documentation and docstrings
- Create README files with setup, usage, and contribution guidelines
- Document configuration options and environment variables
- Provide troubleshooting guides and code examples
4. User Documentation
- Write step-by-step user guides
- Create getting started tutorials
- Document common workflows and use cases
- Include accessibility and localization notes
5. Documentation Automation
- Configure CI/CD pipelines for automatic doc generation
- Set up documentation linting and validation
- Implement documentation coverage checks
- Automate deployment to hosting platforms
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.
- 4d ago First seen · 653 lines · 0 tokens per session scan A 83083eaa3e15
doc-generate is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,666 tokens. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to doc-generate, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
init
Initialize configurations for Supabase local development.
http-service
Build, review or debug a Bun HTTP service. Loads the http-service skill, then works the task through its workflow.
start-10-1
A guided lesson on setting up clasp, a command-line tool for managing Google Apps Script projects, and connecting it to Google’s Apps Script API.
api-contract-review
Review an API contract (endpoints, request/response shapes, error codes, auth model) BEFORE implementation for naming consistency, versioning, pagination, idempotency, and alignment with existing endpoints. Distinct from review-hard (post-implementation risk) and repo-consistency-sweep (pattern matching on written…
build
Discover an AI Gateway's models and MCP tools, retrieve a credential, and integrate them into your app — call a model, connect MCP tools, or scaffold a runnable agent.
fastapi
FastAPI application design and implementation conventions. Use this skill when building, updating, or reviewing FastAPI services, routers, dependencies, request/response schemas, streaming endpoints, or API tests. Trigger on FastAPI-specific work such as path operation design, dependency injection, response models…