agentleFS
DROOdotFOODROOdotFOO/agent-skills/skills/codebase-onboarding/SKILL.md

codebase-onboarding

Skill1 starsChanged 2 months agoMITView on GitHub

Auto-generate onboarding documentation from codebase analysis, tailored to the reader's experience level. TRIGGER when: user asks to onboard someone to a codebase, document this codebase, generate project documentation for new team members, write a codebase walkthrough or tour, create a getting-started guide, or explain a codebase to a specific audience. DO NOT TRIGGER when: user wants API reference docs (use language-specific tooling), or wants to understand a single file (just read it).

  • Reads credentials

We split this skill into the rules it gives an agent

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

  • 0

    Common

    Most of the 47 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep the glossary to terms a newcomer would not know

    line 58
  2. Distinctive:

    If the audience is not specified, ask before generating

    line 60

“Common” is measured: kept by at least 6 in 10 of the 47 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

codebase-onboarding

Auto-generate onboarding documentation from codebase analysis. Audience-aware output for junior developers, senior engineers, and contractors.

What You Get

  • Architecture overview derived from actual code structure
  • Key entry points and control flow
  • Testing strategy and deployment process
  • Domain glossary extracted from code and comments
  • Audience-tailored depth and focus

Workflow

  1. Analyze -- Scan the codebase to discover stack, structure, and patterns
  2. Capture signals -- Identify entry points, config files, CI/CD, test patterns, dependency graph, domain terms
  3. Fill template -- Populate the output format sections
  4. Tailor -- Adjust depth and emphasis for the target audience

Analysis signals

SignalWhere to look
Language/frameworkpackage.json, mix.exs, go.mod, Cargo.toml, pyproject.toml
Entry pointsmain files, router definitions, CLI entry points
Config.env.example, config/, settings files
CI/CD.github/workflows/, Makefile, Dockerfile
Teststest/, spec/, tests/, *_test.go
Databasemigrations/, schema files, ORM models
API surfaceOpenAPI specs, GraphQL schema, route definitions
Domain termsREADME, doc comments, module names, type names
DependenciesLock files, vendor/, package manifests
Dev setupMakefile, docker-compose.yml, devcontainer.json

Rules

  1. Derive everything from the actual codebase -- do not fabricate
  2. Flag gaps explicitly ("No CI/CD configuration found")
  3. Keep the glossary to terms a newcomer would not know
  4. Link to actual files using relative paths
  5. If the audience is not specified, ask before generating

Reference

FileTopic
audience-profiles.mdJunior/Senior/Contractor focus areas
output-template.mdMarkdown template for the onboarding doc

Download now (ZIP)

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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.