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.
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Files
SKILL.md
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Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
