Skip to content
project-onboarding logo

Efficient file listing (respects .gitignore)

project-onboarding

Guided project onboarding for new codebases. Helps agents understand project structure, build systems, test commands, and development workflows by creating persistent knowledge memories.

oimiragieo/agent-studio0installs43stars

SKILL.md

Full skill instructions

<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.

More skills from oimiragieo

cloud-devops-expert logo
oimiragieo/agent-studio

cloud-devops-expert

Cloud and DevOps expert including AWS, GCP, Azure, and Terraform

43 0
View
xlsx logo
oimiragieo/agent-studio

xlsx

Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.

43 0
View
project-analyzer logo
oimiragieo/agent-studio

project-analyzer

Automated brownfield codebase analysis with weighted keyword scoring, three-stream analysis, and design pattern recognition. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile.

43 0
View
readme logo
oimiragieo/agent-studio

readme

Use when creating, updating, or generating README and documentation files for projects and libraries

43 0
View
scientific-writing logo
oimiragieo/agent-studio

scientific-writing

Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figure...

43 0
View
android-expert logo
oimiragieo/agent-studio

android-expert

Comprehensive Android development expert covering Jetpack Compose, Kotlin coroutines/Flow, Architecture Components, Hilt DI, Navigation, testing, performance, Material Design 3, and modern Android patterns (MVI, Clean Architecture).

43 0
View
biorxiv-database logo
oimiragieo/agent-studio

biorxiv-database

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

43 0
View
omega-gemini-cli logo
oimiragieo/agent-studio

omega-gemini-cli

Use when the user wants to use Google Gemini for analysis, large files or codebases, sandbox execution, or brainstorming. Uses headless Gemini CLI scripts (no MCP). Triggers on "use Gemini", "analyze with Gemini", "large file", "sandbox", "brainstorm with Gemini".

43 0
View
gtars logo
oimiragieo/agent-studio

gtars

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

43 0
View
service-class-conventions logo
oimiragieo/agent-studio

service-class-conventions

Defines the structure and implementation of service classes, enforcing the use of interfaces, ServiceImpl classes, DTOs for data transfer, and transactional management.

43 0
View
diffdock logo
oimiragieo/agent-studio

diffdock

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

43 0
View
skill-discovery logo
oimiragieo/agent-studio

skill-discovery

How agents discover and use skills. Use to understand skill invocation protocol.

43 0
View

Popular AI tools

Kaiber logo
Video

Kaiber

Generate, edit, and beat-sync AI video with leading models in one workspace.

Paid
View
Vimcal logo
Productivity

Vimcal

The world's fastest calendar for remote work

Free
View

Transform Your Design with AI Designer by ImgCreator.ai

Freemium
View
Akool AI logo
Content & writing

Akool AI

Revolutionizing Video Production with AI-Powered Creativity

Paid
View

Extend an image past the frame and let AI fill the new aspect ratio.

Freemium
View
StarByFace logo
Security

StarByFace

Discover your celebrity doppelgänger with StarByFace!

Free
View
C

ChainClarity explains 700+ crypto whitepapers in plain English, with layered summaries, comparisons, research tools, alerts, and a $4.99 Pro plan.

Freemium
View
Opus Clip logo
Coding & apps

Opus Clip

Opus.ai: Revolutionize Your Web Experience

Free
View