Project Code Builder

What This Skill Does

The Project Code Builder by chleya is an advanced intelligent automation agent designed for the complete lifecycle of software development. Unlike standard AI coding assistants, it functions as an autonomous engineer capable of managing everything from initial project architecture to creating GitHub Pull Requests. It integrates deep tool-calling capabilities with long-term context memory, specifically engineered for high-performance 2026 workflows. It handles file operations, local Git version control, dependency management, and automated testing, allowing developers to manage complex codebases with minimal manual intervention.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/chleya/project-code-builder

Ensure that you have the required dependency skills installed:

• /install skill github (for repository management)
• /install skill git (for version control)
• /install skill code-analyzer (for parsing and generating code)
• /install skill tester (for validation)

After installation, update your moltbot.json configuration to set thinking to minimal and enableMemory to true to ensure optimal performance and token usage.

Use Cases

• Rapid Prototyping: Quickly spin up new repositories with scaffolded structures for web APIs or microservices.
• Feature Implementation: Develop modular features by isolating tasks into specific sessions to prevent context pollution.
• Automated Bug Squashing: Execute automated debug loops to identify, fix, and verify code errors with minimal human oversight.
• CI/CD Lifecycle Management: Automate the bridge between local development and GitHub, including automatic branch management and PR generation.

Example Prompts

1. "Plan the project structure for a Python-based FastAPI service including SQLite database integration and JWT authentication, output as a JSON tree."
2. "Implement the GET /users endpoint in app.py, ensuring it fetches data from the database, then commit the changes to the current branch."
3. "Run unit tests for the current module using pytest, generate missing tests if necessary, and output only the summary of the results."

Tips & Limitations

• Token Optimization: Always prefer modular development by creating new sessions for specific features. This prevents context bloat and keeps your token consumption low.
• Configuration: Always set your model to a coding-optimized LLM like deepseek-coder-v2 for the best results.
• Safety: Since this skill performs file writes and external API calls, review PRs before merging. Use /reset frequently to clear workspace state and maintain optimal performance in long-running projects.