Codebase Onboarding
SkillAI & modelsOnce added, your AI can study a codebase and turn it into clear onboarding documentation for engineers, tech leads, and contractors. It gathers the important facts quickly and produces the same kind of write-up each time, so new team members can get up to speed on unfamiliar projects.
Available today. Use it from your connected AI after setup.
No other account needed.
Point your AI at the codebase you want documented and ask for an onboarding guide, an architecture overview, or a tech-lead briefing. Reuse the same request for each new project or person joining the team.
Then ask your AI: use the Codebase Onboarding skill
What your AI can do with it
- Analyze an unfamiliar codebase and gather the key facts
- Generate onboarding documentation for new engineers
- Write architecture-overview docs for a new project
- Produce tech-lead briefings for unfamiliar repositories
- Create onboarding materials for contractors joining a project
- Repeat the same onboarding output across projects and new hires
What this skill tells your AI
The instructions your AI receives, as published by alirezarezvani/claude-skills in .gemini/skills/codebase-onboarding/SKILL.md and read by ahel’s review.
Tier: POWERFUL Category: Engineering Domain: Documentation / Developer Experience
Overview
Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. This skill is optimized for fast fact-gathering and repeatable onboarding outputs.
Core Capabilities
- Architecture and stack discovery from repository signals
- Key file and config inventory for new contributors
- Local setup and common-task guidance generation
- Audience-aware documentation framing
- Debugging and contribution checklist scaffolding
When to Use
- Onboarding a new team member or contractor
- Rebuilding stale project docs after large refactors
- Preparing internal handoff documentation
- Creating a standardized onboarding packet for services
Quick Start
# 1) Gather codebase facts
python3 scripts/codebase_analyzer.py /path/to/repo
# 2) Export machine-readable output
python3 scripts/codebase_analyzer.py /path/to/repo --json
# 3) Use the template to draft onboarding docs
# See references/onboarding-template.md
Recommended Workflow
- Run
scripts/codebase_analyzer.pyagainst the target repository. - Capture key signals: file counts, detected languages, config files, top-level structure.
- Fill the onboarding template in
references/onboarding-template.md. - Tailor output depth by audience:
- Junior: setup + guardrails
- Senior: architecture + operational concerns
- Contractor: scoped ownership + integration boundaries
Onboarding Document Template
Detailed template and section examples live in:
references/onboarding-template.mdreferences/output-format-templates.md
Common Pitfalls
- Writing docs without validating setup commands on a clean environment
- Mixing architecture deep-dives into contractor-oriented docs
- Omitting troubleshooting and verification steps
- Letting onboarding docs drift from current repo state
Best Practices
- Keep setup instructions executable and time-bounded.
- Document the "why" for key architectural decisions.
- Update docs in the same PR as behavior changes.
- Treat onboarding docs as living operational assets, not one-time deliverables.
Signals
- GitHub stars
- 26k
- Forks
- 4k
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
codebase-onboarding-alirezarezvani- Source
- github.com/alirezarezvani/claude-skills