Coordinate AI coding agents across the full development lifecycle.
npx skills add https://github.com/shin0720/autopus --skill auto-shin0720
Please help me install this Agent Skill. Skill: auto Source: https://github.com/shin0720/autopus/tree/main/.gemini/skills/auto Command: npx skills add https://github.com/shin0720/autopus --skill auto-shin0720
This Skill eliminates the complexity of coordinating multiple AI coding agents through the full software development lifecycle, replacing fragmented manual workflows with a single unified command interface.
/auto dev "Add OAuth2 with Google and GitHub providers" and the system will automatically plan, implement with parallel agents, run tests, perform security audits, and generate documentation.Use the auto skill to execute a full development cycle by typing /auto dev "your feature description" in your AI coding agent's chat.
High-intent search queries and answers about installing and using this skill.
Automating AI coding agents across the software development lifecycle requires a unified command interface that coordinates planning, implementation, testing, and security auditing. This skill uses intelligent triage to route natural language requests to specialized agents across multiple providers.
Multi-provider orchestration in AI development workflows coordinates specialized agents across different providers to handle tasks from SPEC generation to code review. It automatically assesses request difficulty and routes development commands to the appropriate workflow stage.
Running code review and security audits automatically after implementation is achieved by triggering a full development pipeline command. The system manages the complete cycle from implementation to testing and security audits using specialized AI agents.
Automated AI agent orchestration requires project context documents and SPEC path resolution to function. The system automatically loads and maintains project architecture, tech stack, and decision history to ensure agents work with consistent understanding across sessions.
Maintaining context continuity across multiple AI agent coding sessions is handled automatically by loading project architecture and decision history. This ensures specialized agents work with consistent understanding without requiring manual context restoration between workflow stages.
Limitations of using a single command hub for AI development workflows include dependencies on orchestra engines for multi-provider decision making and the need for triage-based routing. It requires structured project context documents and SPEC path resolution to execute the full pipeline.
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