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Found 5 Skills
Orchestrate autonomous AI development with task-based workflow and QA gates
AI-Native Issue-Driven development workflow. From GitHub Issue to merged PR: parse issue, explore codebase, design technical plan, execute with agent team, create PR, and cleanup. Use when a user wants to implement a GitHub Issue end-to-end: `/issue-flow #123` or `/issue-flow` to pick from open issues.
Connect the complete AI development workflow through documents. It covers domain modeling and code organization (DDD), behavior verification and automated testing (BDD), as well as AI development specification setting (Agent specifications). Use when (1) the project has .feature files, (2) the user asks to organize code by business features or define naming conventions, (3) creating or updating AGENTS.md / project rule files, (4) writing or implementing Gherkin scenarios, (5) starting a new project from scratch, or (6) the agent needs the full development lifecycle.
Use when annotating code with structured metadata, tags, and markers for AI-assisted development workflows. Covers annotation formats, semantic tags, and integration with development tools.
Use when executing multi-task plans where each task can be implemented independently by a subagent. Triggers when a plan has 3+ independent tasks, when speed of execution is important, when tasks have clear acceptance criteria suitable for delegation, or when two-stage review gates (spec compliance and code quality) are needed for iterative fix cycles.