Execute autonomous development loops to implement and verify quantum.json stories.
npx skills add https://github.com/andyzengmath/quantum-loop --skill ql-execute
Please help me install this Agent Skill. Skill: ql-execute Source: https://github.com/andyzengmath/quantum-loop/tree/main/skills/ql-execute Command: npx skills add https://github.com/andyzengmath/quantum-loop --skill ql-execute
Many AI-driven code generation workflows produce code without running, testing, or validating it against structured requirements, causing flaky or incorrect implementations and wasted review cycles. ql-execute ensures that stories are implemented, tested, and verified end-to-end according to a dependency-aware plan so work does not drift or cascade failures.
Start the autonomous execution loop to implement and verify all stories defined in quantum.json.
High-intent search queries and answers about installing and using this skill.
Autonomous parallel implementation uses a dependency DAG from quantum.json to spawn isolated git worktree agents for test-driven development, ensuring code stories are implemented, verified, and committed safely without cascading failures.
Dependency-aware execution reads a quantum.json dependency DAG to execute eligible stories, keeping blocked work isolated so that AI-driven code generation produces verified, end-to-end implementations without drifting from structured requirements.
Running TDD across isolated git worktrees spawns parallel implementer subagents that apply changes, run build tools, execute tests, and commit safely, enabling reproducible and auditable development loops for multiple features.
Autonomous development loops require a git repository with a quantum.json dependency DAG, available build tools, orchestrator instructions in agents/orchestrator.md, and native support for isolated git worktrees to enable parallel execution.
Code review and automated quality checks are enforced through two-stage verification gates that apply mandatory review checks before marking stories COMPLETE, ensuring all implementations are validated against structured requirements.
AI-generated implementations fail without dependency-aware execution because unvalidated code skips testing and review cycles, causing flaky implementations and cascading failures that drift from structured dependency requirements.
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