Unlazy: The SIMPLE Skill that actually improves PERFORMANCE of ALL AI AGENTS!!
Visit AISeeKing: https://www.youtube.com/@AISeeKingIn 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.
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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. Receive SMS online on sms24.me
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