Generate A-round plans and B-round quality checks for skill optimization.
npx skills add https://github.com/huangwb8/skills --skill auto-test-skill
Please help me install this Agent Skill. Skill: auto-test-skill Source: https://github.com/huangwb8/skills/tree/main/auto-test-skill Command: npx skills add https://github.com/huangwb8/skills --skill auto-test-skill
This skill orchestrates a structured, multi-round critique and testing pipeline for AI skills, generating A-round plans and B-round quality checks to ensure traceable improvements.
Trigger this skill against a target skill root to emit A-round plans and B-round checks automatically.
High-intent search queries and answers about installing and using this skill.
Automate multi-round testing by generating structured A-round evaluation plans and B-round quality checks. This pipeline orchestrates critique and testing to ensure traceable improvements for AI skills in local repositories.
Independent A-round evaluation avoids leakage by isolating the assessment phase from the testing phase. It automatically generates separate planning and testing documents in the plans/ and tests/ directories to maintain strict process separation.
Configure YAML-driven quality thresholds by defining criteria such as min_p0_p1_ratio and min_systemic_issues. The B-round quality checks validate generated plans and tests against these configurable thresholds to ensure measurable improvements.
Yes, multi-round skill optimization supports cross-skill applicability across multiple skills in a local repository. You trigger the testing pipeline against a target skill root to automatically emit A-round plans and B-round checks.
A-round plans focus on independent evaluation and structured critique, while B-round quality checks validate those plans against configurable YAML thresholds. Together, they enforce traceable outputs and ensure measurable skill improvements.
You need YAML-driven threshold validation when enforcing measurable quality criteria during multi-round skill testing. It validates outputs against configurable metrics like min_p0_p1_ratio and min_systemic_issues to guarantee traceable improvements.
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