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Project Onboarding

  • 79 installs
  • 36 repo stars
  • Updated July 14, 2026
  • oimiragieo/agent-studio

Helps with ai & agent building tasks.

About

project-onboarding is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • project-onboarding
  • AI & Agent Building
  • AI-coding skill

Project Onboarding by the numbers

  • 79 all-time installs (skills.sh)
  • +1 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #5,262 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs79
repo stars36
Last updatedJuly 14, 2026
Repositoryoimiragieo/agent-studio

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

<identity> Project Onboarding Specialist - Guided codebase exploration and knowledge capture for rapid project understanding. </identity>

<capabilities>

  • Discovering project structure and organization patterns
  • Identifying build systems and package managers
  • Finding test commands and coverage configuration
  • Mapping key directories and entry points
  • Creating persistent memories for future sessions
  • Generating project overview documentation
  • Identifying development workflows and conventions

</capabilities>

<instructions>

When to Use

Invoke this skill when:

  • Starting work on an unfamiliar codebase
  • After context is lost (new session)
  • When check_onboarding_performed indicates no memories exist
  • When user asks to "learn about this project" or "understand this codebase"

Onboarding Workflow

Step 1: Check Existing Knowledge

First, check if onboarding was already performed:

List files in: .claude/context/memory/
Look for: project-structure.md, build-commands.md, test-commands.md

If memories exist, read them and skip to Step 6 (Validation).

Step 2: Project Discovery

First, classify the project:

Greenfield vs Brownfield Detection
IndicatorPresent?Classification
.git directory with historyYesBrownfield
Package manifest (package.json, requirements.txt, etc.)YesBrownfield
Source directories (src/, app/, lib/) with codeYesBrownfield
Dirty git status (uncommitted changes)YesBrownfield (warn user)
Empty or only README.mdNone of aboveGreenfield

For Brownfield Projects:

1. Respect Ignore Files: Check .gitignore and .claudeignore BEFORE scanning 2. Efficient File Triage:

  • Use git ls-files to list tracked files (respects .gitignore)
  • For large files (>1MB): Read only head/tail (first and last 20 lines)
  • Skip binary files, node_modules, build artifacts

3. Infer Tech Stack: Analyze manifests before asking questions 4. Context-Aware Questions: Base questions on discovered patterns

# Efficient file listing (respects .gitignore)
git ls-files --exclude-standard -co | head -100

# For non-git projects with manual ignores
find . -type f \
  -not -path '*/node_modules/*' \
  -not -path '*/.git/*' \
  -not -path '*/dist/*' \
  -not -path '*/build/*' \
  | head -100

For Greenfield Projects:

  • Create fresh context artifacts
  • Use interactive-requirements-gathering skill for setup

Analyze the project root to identify:

1. Package Manager & Language:

  • package.json - Node.js/JavaScript/TypeScript
  • pyproject.toml, requirements.txt - Python
  • Cargo.toml - Rust
  • go.mod - Go
  • pom.xml, build.gradle - Java
  • composer.json - PHP

2. Project Type:

  • Frontend, Backend, Fullstack, Library, CLI, Mobile, Monorepo

3. Framework Detection:

  • Parse dependencies for frameworks (React, Next.js, FastAPI, etc.)

Step 3: Build System Analysis

Identify how to build/run the project:

1. Check package.json scripts (Node.js):

   {
     "scripts": {
       "dev": "...",
       "build": "...",
       "start": "...",
       "test": "..."
     }
   }

2. Check Makefiles (Python, Go, Rust):

   build:
   test:
   lint:

3. Check pyproject.toml (Python):

   [tool.poetry.scripts]
   [tool.poe.tasks]

4. Document discovered commands:

  • Development: npm run dev, uv run dev
  • Build: npm run build, cargo build
  • Test: npm test, pytest
  • Lint: npm run lint, ruff check

Step 4: Directory Structure Mapping

Map key directories:

DirectoryPurpose
src/Source code
lib/Library code
test/, tests/, __tests__/Test files
docs/Documentation
scripts/Utility scripts
config/Configuration files

Identify:

  • Entry points (index.ts, main.py, app.py)
  • Component directories
  • API routes
  • Database models

Step 5: Create Onboarding Memories

Save discovered information to persistent memories:

Memory: project-structure.md

# Project Structure

## Overview

- Project Type: [fullstack/backend/frontend/library]
- Primary Language: [TypeScript/Python/Go/Rust]
- Framework: [Next.js/FastAPI/Express/etc.]

## Key Directories

- Source: `src/`
- Tests: `test/`
- Config: `.claude/`

## Entry Points

- Main: `src/index.ts`
- API: `src/api/`

## Important Files

- Configuration: `package.json`, `tsconfig.json`
- Environment: `.env.example`

Memory: build-commands.md

# Build Commands

## Development

- Start dev server: `npm run dev`
- Watch mode: `npm run watch`

## Build

- Production build: `npm run build`
- Type check: `npm run typecheck`

## Clean

- Clean build: `npm run clean`

Memory: test-commands.md

# Test Commands

## Unit Tests

- Run all: `npm test`
- Watch mode: `npm test -- --watch`
- Coverage: `npm test -- --coverage`

## E2E Tests

- Run: `npm run test:e2e`

## Linting

- Lint: `npm run lint`
- Fix: `npm run lint:fix`

Step 6: Validation

Validate discovered information:

1. Test Commands (if safe):

  • Run npm --version or equivalent to verify package manager
  • Run npm run --silent to list available scripts
  • Do NOT run build or test without user permission

2. Verify Paths:

  • Confirm key directories exist
  • Verify entry points are correct

Step 7: Report Summary

Output a concise summary:

## Onboarding Complete

**Project**: [name]
**Type**: [fullstack/backend/etc.]
**Framework**: [Next.js/FastAPI/etc.]

**Quick Commands**:
- Dev: `npm run dev`
- Test: `npm test`
- Build: `npm run build`

**Key Locations**:
- Source: `src/`
- Tests: `test/`
- API: `src/api/`

**Memories Created**:
- .claude/context/memory/project-structure.md
- .claude/context/memory/build-commands.md
- .claude/context/memory/test-commands.md

</instructions>

<examples> <usage_example> User Request: "I need to understand this codebase"

Agent Actions:

1. Check for existing memories in .claude/context/memory/ 2. If no memories, run project discovery 3. Analyze package.json, directory structure 4. Create memory files 5. Report summary

Output:

## Onboarding Complete

**Project**: agent-studio
**Type**: Multi-agent orchestration framework
**Framework**: Claude Code + Custom agents

**Quick Commands**:
- Validate: `node .claude/tools/cli/validate-agents.mjs`
- Test hooks: `node .claude/hooks/routing/router-enforcer.cjs` (uses `.claude/lib/routing/routing-table.cjs`)

**Key Locations**:
- Agents: `.claude/agents/`
- Skills: `.claude/skills/`
- Memory: `.claude/context/memory/`

**Memories Created**: 3 files

</usage_example> </examples>

<integration> Related Skills:

  • project-analyzer - Deep automated analysis (complements onboarding)
  • repo-rag - Semantic search for patterns
  • session-handoff - Prepare context for new sessions

</integration>

Iron Laws

1. ALWAYS check for existing onboarding memories before running discovery — duplicate onboarding creates contradictory knowledge entries that mislead future agents. 2. NEVER assume standard conventions without verification — run commands to confirm what actually works in this specific project environment. 3. ALWAYS write discovered commands and paths to persistent memory files — session context resets, but named memory files survive indefinitely. 4. ALWAYS verify discovered commands with a safe test step before documenting — incorrect commands in onboarding memories mislead every agent that reads them. 5. NEVER complete onboarding without revisiting memories when the project evolves — stale onboarding artifacts are more dangerous than no onboarding.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Assuming standard conventions without checkingEvery project has unique build/test/lint commands; wrong assumptions cause silent failuresRead package.json, Makefile, or pyproject.toml and run --version to confirm
Skipping verification of discovered commandsDocumented-but-wrong commands mislead every future agent sessionRun each command with a safe no-op or --help flag to confirm it works
Storing onboarding only in session contextContext resets on every new conversation; discoveries are permanently lostWrite all findings to named memory files in .claude/context/memory/named/
Treating onboarding as a one-time eventProjects evolve; stale commands fail silently and waste agent timeUpdate onboarding memories after any significant project structure change
Over-documenting without prioritizing key commandsLong files with low-priority info bury the critical build/test commandsStructure memories with Quick Start commands at the top, details below

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

  • New pattern discovered -> .claude/context/memory/learnings.md
  • Issue encountered -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

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