Skill Benchmark

skill-benchmark

Benchmark a skill from multiple perspectives — structure quality, description triggering, token efficiency, documentation completeness, and real-world value-add — then produce a scored report with improvement suggestions. Three analyst agents run in parallel to keep the process fast.

For official Anthropic token budget guidelines, see references/token-guidelines.md in this skill's directory.

Agents

Step 1: Resolve the target skill

The user provides a skill name as an argument (e.g., /skill-benchmark readme). Resolve it to a SKILL.md file path.

Use the Glob tool to search:

plugins/*/skills/{argument}/SKILL.md

If no match, list all available skills by globbing plugins/*/skills/*/SKILL.md and present the skill names. Use AskUserQuestion to let the user pick.

If the argument matches a plugin with multiple skills (e.g., gh contains gh-issue, gh-dev, gh-pr), list all skills inside that plugin and ask the user to specify.

If the argument is a file path, use it directly after confirming the file exists.

Store the resolved path as TARGET_SKILL_PATH and its parent directory as TARGET_SKILL_DIR.

Step 2: Inventory the skill

Read the full content of the target SKILL.md using the Read tool. Store as SKILL_CONTENT.

Then inventory the skill's bundled resources by globbing each subdirectory:

{TARGET_SKILL_DIR}/agents/*.md
{TARGET_SKILL_DIR}/scripts/*
{TARGET_SKILL_DIR}/assets/*
{TARGET_SKILL_DIR}/references/*
{TARGET_SKILL_DIR}/templates/*
{TARGET_SKILL_DIR}/evals/evals.json

Compile the results into SKILL_INVENTORY — a structured summary like:

agents: scout.md, explainer.md, analyst.md, checker.md (4 files)
scripts: none
assets: template.md, template-zh-TW.md (2 files)
references: none
templates: none
evals: evals.json present (3 test cases)

Briefly inform the user what you found: "Benchmarking {skill-name} — found {N} agents, {N} scripts, {N} assets. Launching 3 analyst agents in parallel..."

Step 3: Launch analyst agents in parallel

Read the three agent definition files from this skill's agents/ directory:

• agents/structure-doc-analyst.md
• agents/description-analyst.md
• agents/value-add-tester.md

Spawn all three using the Agent tool in a single message (three Agent tool calls). Pass each agent the data it needs:

Structure & Doc Analyst prompt:

You are the Structure & Documentation Analyst. Read your instructions at:
{this-skill-path}/agents/structure-doc-analyst.md

Then analyze this skill:
- SKILL_CONTENT: {full SKILL.md text}
- SKILL_INVENTORY: {inventory summary}
- TARGET_SKILL_PATH: {path}

Follow your output format exactly.

Description Analyst prompt:

You are the Description & Triggering Analyst. Read your instructions at:
{this-skill-path}/agents/description-analyst.md

Then analyze this skill:
- SKILL_CONTENT: {full SKILL.md text}

Follow your output format exactly.

Value-Add Tester prompt:

You are the Value-Add Tester. Read your instructions at:
{this-skill-path}/agents/value-add-tester.md

Then analyze this skill:
- SKILL_CONTENT: {full SKILL.md text}
- TARGET_SKILL_PATH: {path}
- SKILL_INVENTORY: {inventory summary}

Follow your output format exactly.

Wait for all three agents to complete. Store their outputs as STRUCTURE_DOC_ANALYSIS, DESCRIPTION_ANALYSIS, and VALUE_ADD_ANALYSIS.

Step 4: Compile scores and generate report

Extract the scores from each agent's output by parsing their **Score: N/10** lines:

• Structure Quality score → STRUCTURE_SCORE
• Documentation Completeness score → DOCUMENTATION_SCORE
• Token Efficiency score → TOKEN_SCORE
• Description Quality score → DESCRIPTION_SCORE
• Value-Add score → VALUE_ADD_SCORE
• Efficiency score → EFFICIENCY_SCORE

Compute the weighted overall score:

overall = (STRUCTURE * 2.0 + DOCUMENTATION * 1.5 + TOKEN * 1.5 + DESCRIPTION * 2.0 + VALUE_ADD * 3.0 + EFFICIENCY * 1.5) / (2.0 + 1.5 + 1.5 + 2.0 + 3.0 + 1.5)

Round to one decimal place.

Read the report template from this skill's assets/template.md. Fill all {PLACEHOLDER} values:

• {SKILL_NAME} — the target skill's name from frontmatter
• {DATE} — today's date
• {SKILL_PATH} — the resolved TARGET_SKILL_PATH
• {RESOURCE_SUMMARY} — the SKILL_INVENTORY summary
• {STRUCTURE_SCORE}, {DOCUMENTATION_SCORE}, {TOKEN_SCORE}, {DESCRIPTION_SCORE}, {VALUE_ADD_SCORE}, {EFFICIENCY_SCORE}, {OVERALL_SCORE} — the scores
• {STRUCTURE_ANALYSIS} — from Structure & Doc Analyst output (the Structure Quality section)
• {DOCUMENTATION_ANALYSIS} — from Structure & Doc Analyst output (the Documentation Completeness section)
• {TOKEN_ANALYSIS} — from Structure & Doc Analyst output (the Token Efficiency section)
• {DESCRIPTION_ANALYSIS} — from Description Analyst output
• {VALUE_ADD_ANALYSIS} — from Value-Add Tester output (both Value-Add and Efficiency sections)
• {IMPROVEMENTS} — collated from all three agents (see Step 5)
• {RECOMMENDATION} — your synthesis (see Step 5)

Step 5: Write recommendation and present report

Collate all improvement suggestions from the three agents into a single prioritized list. Group by priority level:

### High Priority
- {suggestion from any agent}

### Medium Priority
- {suggestion}

### Low Priority
- {suggestion}

Write a {RECOMMENDATION} section — 2-4 paragraphs synthesizing the scores into an overall assessment. Address these questions:

• Is this skill worth keeping? (Reference the Value-Add score — if it's 4 or below, suggest the user consider whether the skill earns its place)
• What's the single most impactful improvement? (Reference the lowest-scoring dimension)
• Is the skill mature or does it need significant work? (Reference Structure and Documentation scores)

The recommendation should be nuanced — not a binary "keep or deprecate" but a contextualized assessment with specific next steps.

Present the completed report directly in chat. If the user asks to save it, write it to {TARGET_SKILL_DIR}/benchmark-report.md.

Hard rules

• Never modify the target skill's SKILL.md or any of its resources. This skill is read-only analysis.
• Never fabricate test results. If live testing cannot be performed, say so and explain why.
• All scores must be integers from 1 to 10. Never use 0 or scores above 10.
• The overall score must be a computed weighted average, not a subjective number. Show the formula.
• Each score must have at least two sentences of written justification. Never present a bare number.
• When generating test prompts for the Value-Add Tester, base them on what the skill claims to do. Never test capabilities the skill does not claim.
• Present the report in chat by default. Only write to file if the user explicitly requests it.
• Never invent improvement suggestions that contradict the skill's stated purpose or design philosophy.