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Get Started Free →Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -46% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -45% | 0% |
> !IMPORTANT] > Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
Optional args: slug=<feature>, ticket=<id/url>, mode=interactive|autonomous|channel, channel=<id>, 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.
bash# 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 your scorecard from evals/evals.json, not from hardcoded patterns. 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 the scorecard and compliant score using the templates in <SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
For every ❌ FAIL, identify the root cause using the Iteration Table in: <SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
Recommend any skills that are noisy or non-applicable for the project.
yamlexclude: - [skill-id] # reason
Other measured skills in the registry, with their headline benchmark lift.