Build the evaluation discipline that separates production agentic products from demos — error analysis on real traces, the three-level eval pyramid (code assertions / LLM-as-judge / human review), binary judge outputs calibrated against human labels, and CI gates that block regression. Based on the Husain/Shankar methodology. Use whenever the user mentions evals, evaluation, LLM-as-judge, hallucination testing, regression testing for AI, quality measurement, error analysis, "how do I know if my agent works," failure modes, or grading agent outputs.
How to install eval-driven-dev?
moai-team-llc/agentic-product-standard/eval-driven-dev
$npx -y skills add moai-team-llc/agentic-product-standard --skill eval-driven-devInstalls into the current project.
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Run `npx skills use "https://github.com/moai-team-llc/agentic-product-standard" --skill "moai-team-llc/agentic-product-standard/eval-driven-dev"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/moai-team-llc/agentic-product-standard" that are relevant to the current task. Run `npx skills add "https://github.com/moai-team-llc/agentic-product-standard"` and select the relevant skills, then follow their instructions.
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