Skill Benchmark Skill
SkillAI & modelsBenchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Skill Benchmark Skill skill
What this skill tells your AI
The instructions your AI receives, as published by hoangnguyen0403/agent-skills-standard in .codex/skills/skill-benchmark/SKILL.md and read by ahel’s review.
[!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.
Instructions
When the user asks to perform this workflow, execute the following steps:
📊 Skill Benchmark Orchestrator
Goal: Quantify how much active skills improve implementation quality. Deliver a prioritized compliance delta and skill applicability report.
Step 1 — Project Context & Active Skills
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
Step 2 — Auto-Select a Legacy Trap
Pick the file automatically. Rank candidates by the severity of anti-patterns:
- 🔴 P0: Hardcoded secrets; Logic inside UI components.
- 🟠 P1: Wrong Router pattern; Global state for local concerns; Missing design tokens.
- 🟡 P2: Raw user-facing strings (i18n).
Step 3 — Build Eval-Driven Scorecard
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:
- Read
<SKILLS>/<category>/<skill>/evals/evals.json. - Generate columns for Failure Pattern and Success Pattern.
- Refactor the file, citing the exact skill rule for each change.
- For guardrail skills, read
pressure_scenarios,rationalizations,red_flags, andbehavior_assertions.
Step 4 — Benchmark Report & Compliance Delta
Output the scorecard and compliant score using the templates in <SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
- Compliance Score Before vs After.
- Δ Delta: +Z% 🚀.
- Eval Alignment: How well does the skill teach what the eval tests?
- Behavior Coverage: pressure scenarios, rationalizations, red flags, behavior assertions.
Step 5 — Skill Applicability & Iteration
For every ❌ FAIL, identify the root cause using the Iteration Table in:
<SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
- Signal not matching file? → Refine trigger.
- Rule too vague? → Add Anti-Pattern rule.
- Conflict? → Ensure P0 overrides P1.
- Guardrail weak under pressure? → Add rationalization counters and red flags.
Suggested .skillsrc Exclusions
Recommend any skills that are noisy or non-applicable for the project.
exclude:
- [skill-id] # reason
Signals
- GitHub stars
- 565
- Forks
- 163
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
skill-benchmark- Source
- github.com/hoangnguyen0403/agent-skills-standard