Stop Being Your Agent's QA: A Skill That Makes AI Coding Agents Prove Their Work

A developer created 'stop-manual-testing', a skill for AI coding agents that requires them to build and run machine-checkable verification criteria before declaring a task complete. The skill aims to eliminate manual QA by enabling agents to self-converge in a closed loop, reducing the time developers spend reviewing agent output.

Every AI coding session ends the same way: the agent says "Done ✓" — and you spend the next 30 minutes manually clicking through everything to find out it isn't. I got tired of being my agent's test suite. So I built stop-manual-testing — a skill that flips the loop: every task ends with machine-checkable criteria the agent must actually run and pass before it's allowed to say "done". It's modular, checks can run in parallel, and it works across agent-driven workflows. A skill that stops you from manually testing your AI agent. The agent reads it, builds a machine-checkable verification system, and self-converges in a closed loop — collapsing the ~90% of dev time you spend staring at runs and judging by gut feel. 中文 · English A skill https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview for AI coding agents Claude Code / Codex / ZCode / Cursor . Load it once, and instead of you manually clicking through the UI and eyeballing whether the agent "got better or worse this run," the agent builds itself a verification system where correctness is machine-checkable — then iterates inside a closed loop until it converges. If you develop AI agents, you are likely stuck here: If you're spending more time reviewing your agent's work than actually creating, give it a try — feedback welcome.