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skill-benchmark【开发与工程】Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
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[!IMPORTANT] Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
Optional args: slug=, ticket=<id/url>, mode=interactive|autonomous|channel, channel=, auto_continue=true|false, profile=business|hybrid|technical.
When the user asks to perform this workflow, execute the following steps:
Goal: Quantify how much active skills improve implementation quality. Deliver a prioritized compliance delta and skill applicability report.
Identify the tech stack and all active skills in AGENTS.md.
# 1. Total source files and lines changed
find src -name "*.ts" -o -name "*.tsx" | xargs wc -l 2>/dev/null | sort -rn | head -20
# 2. Check active skill registry
cat AGENTS.md | head -80
Pick the file automatically. Rank candidates by the severity of anti-patterns:
Source scorecard from evals/evals.json. Distinguish structural checks, textual transcript assertions, and executable verifier outcomes. Never add canned assertion keywords to skills to artificially inflate scores (no answer-anchor padding).
Follow the Scorecard Rubric in <SKILLS>/common/common-skill-creator/references/benchmark.md when synced:
<SKILLS>/<category>/<skill>/evals/evals.json.pressure_scenarios, rationalizations, red_flags, and behavior_assertions.Output scorecard and evidence breakdown using templates in <SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
For every failure or candidate rule, evaluate using the Iteration Table and ablation criteria in benchmark.md:
Recommend any skills that are noisy or non-applicable for the project.
exclude:
- [skill-id] # reason