A comprehensive project lifecycle tool for AI agents that handles initialization, architectural analysis, and automated Q&A documentation across 50+ project types.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install project-assistant
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install project-assistant using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Project Assistant?
Project Assistant is a versatile tool within the Openclaw Skills ecosystem designed to streamline how AI coding agents interact with complex codebases. It serves as a bridge between raw source code and high-level architectural understanding, enabling agents to provide expert-level insights whether they are acting as an Architect, Developer, or Project Manager.
By supporting over 50 project types—ranging from embedded systems like STM32 to modern web frameworks like React and FastAPI—this skill ensures that your AI agent always has the correct context. It automates the tedious parts of project management, such as maintaining documentation and tracking call chains, allowing developers to focus on building features rather than deciphering structures.
Project Assistant Use Cases
- Initializing new projects with standardized structures and AI-ready metadata.
- Performing deep architectural analysis to understand system scalability and bottlenecks.
- Generating automated impact reports before making significant code changes.
- Managing persistent Q&A documentation to reduce repetitive queries during development.
- Synchronizing project status and documentation suggestions with Feishu/Lark for team collaboration.
How Project Assistant Works
- Directory Discovery: The skill determines the working directory by checking CLI arguments, configuration settings, or the current environment.
- Project Inspection: It searches for the project metadata file; if missing, it triggers an initialization routine to prepare the workspace.
- Cache & Strategy Mapping: Based on the query type (e.g., Architecture vs. Impact), it selects an optimized caching strategy to ensure data freshness.
- Contextual Search: It queries existing Q&A documentation and historical answers to find relevant local knowledge.
- Intelligent Analysis: The skill analyzes code logic, call chains, or configuration files to generate a precise answer tailored to the user's role.
- Knowledge Solidification: The final answer is saved back into the project's Q&A repository for future reference.
Project Assistant Setup
To integrate this into your workflow, ensure you have Python 3.6+ installed. You can configure and initialize your project using the following commands:
# Set your working directory
python3 scripts/config_manager.py {baseDir} set workdir /path/to/your/project
# Initialize the project assistant metadata
python3 scripts/init.py /path/to/your/project
# Check the current configuration
python3 scripts/config_manager.py {baseDir} show-config
Project Assistant Data Schema & Taxonomy
Project Assistant organizes information into a structured hierarchy within the project's .claude/ or references/ directories:
| Data Component | Format | Description |
|---|---|---|
| Project Metadata | YAML/Markdown | Stores project type, tech stack, and entry points. |
| Q&A Repository | Markdown | Persistent storage of historical questions, answers, and tags. |
| Cache Store | Binary/JSON | Optimized structural data for fast architectural lookups. |
| Feishu Config | JSON | Integration tokens and sync status for team reporting. |
| Call Chains | Text/Graph | Indexed relationships between functions and modules. |
Project Assistant Advanced Features
- Multi-perspective Analysis: Switches logic based on whether the user needs an Architect's view or a DevOps perspective.
- Smart Impact Analysis: Analyzes function call chains to predict the ripple effects of code modifications.
- Feishu/Lark Integration: Automatically generates status reports and documentation suggestions for team communication.
- Cross-Platform Support: Deep support for Embedded (MCU/RTOS), Mobile (iOS/Android), and Web stacks.
- Automated Cache Management: Intelligent timestamp checking to balance speed and accuracy during large-scale analysis.
SKILL.md
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