Summary
This is about building self-correcting workflows instead of hoping your prompts work on the first try. You define quality criteria with weights and thresholds, pick an evaluator type (rule-based, cross-model, human-in-the-loop), then set up a correction loop that feeds specific feedback back into retries. The key insight is that retries must include the evaluator's feedback, not just run the same input again. It includes regression detection against golden test sets and production monitoring patterns. If you're building anything where output quality matters and you need more than crossing your fingers, this gives you the scaffolding to make it converge reliably.
Install to Claude Code
npx -y skills add sharpdeveye/maestro --skill iterate --agent claude-codeInstalls into .claude/skills of the current project.
Keeps your Mac awake while Claude Code, Codex or Cursor works. Lets it sleep when they are done.
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Block distracting apps from your iPhone permanently without a 3rd party app. Free and open source.
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Notes, actions and memory. Without a meeting bot. First month 100% off.
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belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
Free an open-source skills that make AI coding agents easier to understand, verify, and control.
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Connect your Claude agent to live crypto prices and trading routes via 1inch
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Keeps your Mac awake while Claude Code, Codex or Cursor works. Lets it sleep when they are done.
Try free for 7 days →Block distracting apps from your iPhone permanently without a 3rd party app. Free and open source.
Block now (100% free) →Notes, actions and memory. Without a meeting bot. First month 100% off.
Download for free →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →Free an open-source skills that make AI coding agents easier to understand, verify, and control.
Download for free →Connect your Claude agent to live crypto prices and trading routes via 1inch
Get the MCP →
