Unlazy: The SIMPLE Skill that actually improves PERFORMANCE of ALL AI AGENTS!!
Visit AISeeKing: https://www.youtube.com/@AISeeKing In this video, I'll be telling you about unlazy, a GitHub repo designed to fix one of the biggest problems in AI coding right now: agents confidently saying work is done when the code is incomplete, untested, or full of placeholders. It introduces runnable acceptance gates, evidence-based verification, approval safety, and orchestration workflows to make AI agent completion much more reliable. -- Key Takeaways: 🚀 unlazy helps AI coding agents prove completion with runnable acceptance gates instead of vague “done” reports. 📋 The GATES.md acceptance ledger defines observable outcomes, CHECK commands, EXPECT markers, and evidence lines. ✅ A gate only counts as met when the command exits successfully and the expected output is actually found. 🔒 The approval system prevents unreviewed shell commands from running and binds approvals to exact commands, paths, shells, and environments. 🔁 The --reverify mode re-runs gates instead of trusting old evidence, making it useful for returned or delegated work. 🌳 Depth Tree orchestration breaks large tasks into scoped leaves with ownership rules, integration gates, and parent verification. 🛑 An optional Claude Code Stop hook can block early completion when the ledger still has unmet gates. 📚 The repo is refreshingly honest about its limits, citing research while avoiding unsupported benchmark claims. 👍 Overall, unlazy is a thoughtful, security-conscious way to make AI coding agents less lazy and more evidence-driven.
