project-onboard
NewAnalyze any project directory and generate AGENTS.md for AI context. Use when the user asks to "onboard", "analyze this project", "understand this project", "generate AGENTS.md", or provides a project path they want to understand. Supports auto-detection of Unity, Unreal, Node.js, Python, Rust, Go, Java, C/C++, and general projects.
Summary
md file that gives AI agents instant project context.
- It uses only built-in tools with zero external dependencies, making it the first lightweight, engine-aware onboarding skill for any codebase.
Overview
Copyright (C) 2026 ZionXiaoxiSuOGLocGo SPDX-License-Identifier: GPL-3.0-or-later
Project Onboard
Turn any project directory into an AGENTS.md that gives the AI agent instant project context. Auto-detects project type and applies type-specific analysis rules. Zero external dependencies — uses only built-in tools.
When This Skill Triggers
- •"onboard this project"
- •"analyze my project"
- •"help me understand this project"
- •"generate AGENTS.md for this project"
- •"what does this project do?"
- •User provides a project path without context
Parameters
- •path (required): Project directory path
- •type (optional): Force project type, skip auto-detection. Values:
unity,unreal,nodejs,python,rust,go,java,cpp,general - •output (optional): Where to save AGENTS.md. Default:
<project_root>/AGENTS.md
Execution Flow
Step 1: Confirm the Target
If no path given, ask the user which project to analyze. If path is relative, resolve to absolute. Confirm with user before proceeding.
Step 2: Quick Scan for Project Type (if --type not specified)
Use glob to look for 50-100 top-level entries. Match against the detection table:
| Signature | Type | Rule Pack |
|---|---|---|
Assets/ + ProjectSettings/ | unity | references/unity.md |
Source/ + .uproject | unreal | references/unreal.md |
package.json without Assets/ | nodejs | references/nodejs.md |
pyproject.toml or requirements.txt or setup.py | python | references/python.md |
Cargo.toml | rust | references/rust.md |
go.mod | go | references/go.md |
pom.xml or build.gradle | java | references/java.md |
CMakeLists.txt | cpp | references/cpp.md |
Dockerfile + docker-compose.yml | docker | references/general.md |
*.sql + migrations/ | database | references/general.md |
.glsl or .hlsl files in top-level search | shader | references/general.md |
| None of the above | general | references/general.md |
Check in order. First match wins. If unsure, use general.
Step 3: Load the Rule Pack
Read references/<type>.md for type-specific scanning instructions. Each rule pack defines:
- •Key files to read first
- •Directory structure conventions
- •Dependency file location
- •Entry point location conventions
- •Type-specific patterns to grep for
Step 4: Deep Scan
Follow the rule pack's instructions to:
- Read project metadata (name, version, description)
- Read dependency manifest (which packages/frameworks are used)
- Map directory structure (what each top-level folder contains)
- Identify entry points (main file, startup scene, bootstrap script)
- Find core modules/classes and their responsibilities
- Detect architectural patterns (MVC, ECS, layered, etc.)
Use grep to find key patterns. Use read with 30-80 line limits per file to extract structure without bloating context. Use glob to verify directory contents.
Step 5: Generate AGENTS.md
Write <project_root>/AGENTS.md with EXACTLY this structure:
# [Project Name]
## Basic Information
- **Type**: [Unity/Node.js/Python/etc.]
- **Language**: [primary language]
- **Framework/Engine**: [specific version if known]
- **Key Dependencies**: [3-8 most important packages]
## Directory Structure
[3-10 top-level directories with one-line descriptions]
## Entry Points
- [How to run/start the project]
- [Startup scene/file and what it does]
## Core Architecture
[BULLET LIST of 3-10 key files/classes and their roles. NOT verbose prose.]
## Dependencies
[TABLE: package name | purpose]
## Build & Run
- How to build: [command]
- How to run: [command]
- How to test: [command]
## Notes
- [Anything unusual or non-standard]
- [Known pitfalls if user has recorded any]Step 6: Report
Tell the user what was detected and where AGENTS.md was saved. Offer to add any project-specific notes or pitfalls they want recorded.
Principles
- •Concise: AGENTS.md target is 200-400 lines. Not a novel — a reference card for the AI.
- •Accurate: Don't guess. If unsure, state uncertainty rather than fabricate.
- •Structured: Bullet points and tables over prose. AI parses structure faster.
- •Actionable: Every line should help the AI make better decisions in future conversations.
- •Incremental later: The user can update AGENTS.md by saying "update AGENTS.md with [new information]".
Adding New Project Types
When the user encounters a project type this skill doesn't recognize, offer to create a new rule pack. Ask:
- What is the project type called?
- What file or folder signatures identify it?
- Where are dependencies declared?
- Where are entry points typically found?
- Any special file formats the AI should understand?
Then write the rule pack to references/<name>.md following the existing format.
🚫 Anti-Pitfalls
- •Do NOT run external tools (npm install, cargo build, etc.) — read-only analysis only
- •Do NOT modify any project files except writing AGENTS.md
- •Do NOT read more than 50 files total — focus on the most important ones
- •If the project has 500+ files, rely more on directory structure and less on reading individual files
- •Do NOT generate user-facing documentation — this is for the AI agent's context
- •If
references/<type>.mddoesn't exist yet, usereferences/general.mdand suggest creating a type-specific rule pack
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/project-onboard.md
@project-onboardUse Cases
Usage Examples
/project-onboard path=/home/user/my-project
onboard this project at ./src
generate AGENTS.md for the current directory with type=python
Security Audits
Frequently Asked Questions
What is project-onboard?
This skill auto-detects your project type (Unity, Unreal, Node.js, Python, Rust, Go, Java, C/C++, or general) and generates an AGENTS.md file that gives AI agents instant project context. It uses only built-in tools with zero external dependencies, making it the first lightweight, engine-aware onboarding skill for any codebase.
How to install project-onboard?
To install project-onboard: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/project-onboard.md. Finally, @project-onboard in Claude Code.
What is project-onboard best for?
project-onboard is a agent categorized under General. It is designed for: agent, python, rust. Created by ZionXiaoxiSuOGLocGo.
What can I use project-onboard for?
project-onboard is useful for: When you clone a new repository and need to quickly understand its structure and purpose.; When you want to generate an AGENTS.md file for a project that Claude Code or another AI agent will work on.; When you need to onboard a new team member by providing a comprehensive project overview file.; When you want to force a specific project type analysis for a mixed or ambiguous codebase.; When you need to regenerate AGENTS.md after significant project changes to keep AI context up to date.; When you want to analyze a project without any prior knowledge about its technology stack..