An end-to-end development orchestrator that manages git lifecycles, build checks, and AI-driven implementation for high-quality software delivery.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install developer-agent
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install developer-agent using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Developer Agent is a sophisticated workflow engine designed to bridge the gap between AI code generation and professional DevOps practices. By leveraging Openclaw Skills, this agent ensures that every code change is backed by a 100% understanding of requirements, rigorous build verification, and structured git operations.
Built to work alongside tools like Cursor, it standardizes the development process from initial requirement analysis to final deployment monitoring. It enforces a strict 'build before commit' policy and requires explicit user approval for plans, making it an essential component for teams using Openclaw Skills to maintain high code quality and reliable CI/CD pipelines.
To integrate this capability into your workflow using Openclaw Skills, ensure your local environment is configured with git and pnpm. Follow these steps:
git checkout staging
git pull origin staging
references/model-selection.md and references/cursor-guidelines.md.pnpm build) is operational.The Developer Agent follows a strict organizational structure for managing development metadata and git history. It utilizes a standardized commit message format and generates a comprehensive final report.
| Data Type | Format/Location | Purpose |
|---|---|---|
| Branch Naming | feature/[task-name] | Isolate development work |
| Commit Tags | feat, fix, refactor, style, docs, chore | Semantic versioning and history |
| Final Report | Markdown Template | Deployment and implementation summary |
| Models | references/model-selection.md | Logic for choosing the right LLM |
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