agentleFS
LeonardoEnnesLeonardoEnnes/ai-engineering-toolkit/.agents/skills/generate-docs/SKILL.md

generate-docs

Skill0 starsChanged 9 days agoApache-2.0View on GitHub

Generates comprehensive, production-grade documentation for the project. Invoke when the user asks to document the codebase, architecture, or APIs.

  • Reads credentials

17 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use commit.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • SentrySkill

    commit

    Create commit messages following Sentry conventions. Use when committing code changes, writing commit messages, or formatting git history. Follows conventional commits with Sentry-specific issue references.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 16 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When asked to generate documentation for a project or feature, do not just output a simple README.

    line 9
  2. Very specific:

    Use strict relative Markdown links between documents (e.g., Architecture Guide).

    line 28

“Common” is measured: kept by at least 6 in 10 of the 16 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Documentation Generator (Enterprise Grade)

<instructions> When asked to generate documentation for a project or feature, do not just output a simple README. You MUST act as a Principal Technical Writer and create a structured, interconnected, and rigorous set of Markdown files.

1. Documentation Structure: Unless specified otherwise, generate or suggest the following structure inside a docs/ folder:

  • README.md (Executive summary, core features, quick start guide, system requirements).
  • docs/ARCHITECTURE.md (High-level system design, architectural patterns like Hexagonal/Clean Architecture, data flow, tech stack rationale).
  • docs/API.md (REST/GraphQL contract specifications, request/response DTO payloads, HTTP status codes, and error standard schemas like RFC 7807 Problem Details).
  • docs/CONTRIBUTING.md (Local development setup, environment variables contract, testing instructions, linter rules, and PR lifecycle).

2. Deep Content Extraction:

  • Inspect configuration files (pom.xml, package.json, pyproject.toml, docker-compose.yml) to accurately map infrastructure dependencies, exact library versions, and build scripts.
  • Extract all environment variables from .env.example or configuration beans, detailing types, default values, and whether they are mandatory.
  • Provide copy-pasteable, robust bash commands covering containerized startup (Docker) and manual execution.

3. Visual Architecture (Mermaid.js):

  • You MUST include at least one Mermaid diagram per major file.
  • In ARCHITECTURE.md, use a Component or Sequence diagram mapping how layers interact (e.g., Controller -> Service -> Repository -> DB / Kafka).

4. Interconnectivity & Maintenance:

  • Use strict relative Markdown links between documents (e.g., [Architecture Guide](./docs/ARCHITECTURE.md)).
  • Cross-reference critical business logic files directly in the codebase when explaining complex routines.
</instructions>

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.