Agent Evaluation Skill

Objective, evidence-based quality assessment for agents and skills. Implements a 6-phase rubric: Identify, Structural, Content, Code, Integration, Report. Every finding must cite a file path and line number — no subjective "looks good" verdicts.

Reference Loading Table

Signal	Load These Files	Why
tasks related to this reference	batch-evaluation.md	Loads detailed guidance from batch-evaluation.md.
tasks related to this reference	common-issues.md	Loads detailed guidance from common-issues.md.
tasks related to this reference	report-templates.md	Loads detailed guidance from report-templates.md.
tasks related to this reference	scoring-rubric.md	Loads detailed guidance from scoring-rubric.md.

Instructions

Phase 1: Identify Evaluation Targets

Goal: Determine what to evaluate and confirm targets exist.

Read the repository CLAUDE.md first to understand current standards before evaluating anything. Only evaluate what was explicitly requested — do not speculatively analyze additional agents or skills.

# List all agents
ls agents/*.md | wc -l

# List all skills
ls -d skills/*/ | wc -l

# Verify specific target
ls agents/{name}.md
ls -la skills/{name}/

Gate: All targets confirmed to exist on disk. Proceed only when gate passes.

Phase 2: Structural Validation

Goal: Check that required components exist and are well-formed.

Score every rubric category — never skip a category even if it "looks fine." Parse each required field explicitly rather than eyeballing YAML. Record PASS/FAIL with the line number for each check.

Run score-component.py to get deterministic PASS/FAIL for all structural checks. The script implements the full ADR-031 rubric (frontmatter, operator context, error handling, referenced files, anti-patterns) and outputs per-check results with line references. Do not re-implement these checks inline — read the JSON output and move directly to scoring.

# Deterministic structural checks via score-component.py
python3 scripts/score-component.py agents/{name}.md --json
# or for a skill:
python3 scripts/score-component.py skills/{name}/SKILL.md --json

The JSON output includes results[0].checks (per-check status, earned_points, max_points, detail) and results[0].total (aggregate score). Record each check status from the JSON — do not re-run grep -c for sections the script already covers.

What score-component.py covers (do not duplicate):

• YAML frontmatter fields
• Operator Context section presence
• Error Handling section presence
• Anti-Patterns section presence
• Referenced file existence
• Inline constraint presence

What requires LLM judgment in Phase 3+ (not covered by the script):

• Operator Context item counts (Hardcoded 5-8, Default 5-8, Optional 3-5)
• allowed-tools list format vs comma-separated string
• description pipe format with WHAT + WHEN + negative constraint
• version set to 2.0.0
• Gate presence in Instructions section
• CAN/CANNOT boundaries section
• Anti-rationalization table in References section

Structural Scoring (60 points):

Component	Points	Requirement
YAML front matter	10	All required fields, list format, pipe description
Operator Context	20	All 3 behavior types with correct item counts
Error Handling	10	Section present with documented errors
Examples (agents) / References (skills)	10	3+ examples or 2+ reference files
CAN/CANNOT	5	Both sections present with concrete items
Anti-Patterns	5	3-5 domain-specific patterns with 3-part structure

Integration Scoring (10 points):

Component	Points	Requirement
References and cross-references	5	Shared patterns linked, all refs resolve
Tool and link consistency	5	allowed-tools matches usage, anti-rationalization table present

See references/scoring-rubric.md for full/partial/no credit breakdowns.

Gate: All structural checks scored with evidence. Proceed only when gate passes.

Phase 3: Content Depth Analysis

Goal: Measure content quality and volume.

Do not estimate length by impression — count lines and calculate the score. "Content is long enough" is not a measurement.

# Skill total lines (SKILL.md + references)
skill_lines=$(wc -l < skills/{name}/SKILL.md)
ref_lines=$(cat skills/{name}/references/*.md 2>/dev/null | wc -l)
total=$((skill_lines + ref_lines))

# Agent total lines
agent_lines=$(wc -l < agents/{name}.md)

Depth Scoring (30 points max):

Total Lines	Score	Grade
>1500 (skills) / >2000 (agents)	30	EXCELLENT
500-1500 / 1000-2000	22	GOOD
300-500 / 500-1000	15	ADEQUATE
150-300 / 200-500	8	THIN
<150 / <200	0	INSUFFICIENT

Gate: Depth score calculated. Proceed only when gate passes.

Phase 4: Code Quality Checks

Goal: Validate that code examples and scripts are functional.

A script existing on disk does not mean it works — run python3 -m py_compile on every .py file. Search for placeholder text in every file, not just files that "look incomplete."

1. Script syntax: Run python3 -m py_compile on all .py files
2. Placeholder detection: Search for [TODO], [TBD], [PLACEHOLDER], [INSERT]
3. Code block tagging: Count untagged (bare ```) vs tagged (```language) blocks

# Python syntax check
# Syntax-check any .py scripts found in the skill's scripts/ directory
python3 -m py_compile scripts/*.py 2>/dev/null

# Placeholder search
grep -nE '\[TODO\]|\[TBD\]|\[PLACEHOLDER\]|\[INSERT\]' {file}

# Untagged code blocks
grep -c '```$' {file}

Gate: All code checks complete. Proceed only when gate passes.

Phase 5: Integration Verification

Goal: Confirm cross-references and tool declarations are consistent.

Reference Resolution:

1. Extract all referenced files from SKILL.md (grep for references/)
2. Verify each reference exists on disk
3. Check shared pattern links resolve (../shared-patterns/)

Tool Consistency:

1. Parse allowed-tools from YAML front matter
2. Scan instructions for tool usage (Read, Write, Edit, Bash, Grep, Glob, Task, WebSearch)
3. Flag any tool used in instructions but not declared in allowed-tools
4. Flag any tool declared but never used in instructions

Anti-Rationalization Table:

1. Check that References section links to anti-rationalization-core.md
2. Verify domain-specific anti-rationalization table is present
3. Table should have 3-5 rows specific to the skill's domain

# Check referenced files exist
grep -oE 'references/[a-z-]+\.md' skills/{name}/SKILL.md | while read ref; do
  ls "skills/{name}/$ref" 2>/dev/null || echo "MISSING: $ref"
done

# Check tool consistency
grep "allowed-tools:" skills/{name}/SKILL.md
grep -oE '(Read|Write|Edit|Bash|Grep|Glob|Task|WebSearch)' skills/{name}/SKILL.md | sort -u

# Check anti-rationalization reference
grep -c "anti-rationalization-core" skills/{name}/SKILL.md

Gate: All integration checks complete. Proceed only when gate passes.

Phase 6: Generate Quality Report

Goal: Compile all findings into the standard report format.

Show all test results with individual scores — never summarize as "all tests pass." Sort findings by impact (HIGH / MEDIUM / LOW). Include specific, actionable recommendations with file paths and line numbers. When batch evaluating, show how each item compares to collection averages; do not report "most are good quality" without quantitative data.

This phase is read-only: report findings but never modify agents or skills. Use skill-creator for fixes. Clean up any intermediate analysis files created during evaluation.

Use the report template from references/report-templates.md. The report MUST include:

1. Header: Name, type, date, overall score and grade
2. Structural Validation: Table with check, status, score, and evidence (line numbers)
3. Content Depth: Line counts for main file and references, grade, depth score
4. Code Quality: Script syntax results, placeholder count, untagged block count
5. Issues Found: Grouped by HIGH / MEDIUM / LOW priority
6. Recommendations: Specific, actionable improvements with file paths and line numbers
7. Comparison: Score vs collection average (if batch evaluating)

Issue Priority Classification:

Priority	Criteria	Examples
HIGH	Missing required section or broken functionality	No Operator Context, syntax errors in scripts
MEDIUM	Section present but incomplete or non-compliant	Wrong item counts, old allowed-tools format
LOW	Cosmetic or minor quality issues	Untagged code blocks, missing changelog

Grade Boundaries:

Score	Grade	Interpretation
90-100	A	Production ready, exemplary
80-89	B	Good, minor improvements needed
70-79	C	Adequate, some gaps to address
60-69	D	Below standard, significant work needed
<60	F	Major overhaul required

Gate: Report generated with all sections populated and evidence cited. Evaluation complete.

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Examples

Example 1: Single Skill Evaluation

User says: "Evaluate the test-driven-development skill" Actions:

1. Confirm skills/test-driven-development/ exists (IDENTIFY)
2. Check YAML, Operator Context, Error Handling sections (STRUCTURAL)
3. Count lines in SKILL.md + references (CONTENT)
4. Syntax-check any scripts, find placeholders (CODE)
5. Verify all referenced files exist (INTEGRATION)
6. Generate scored report (REPORT) Result: Structured report with score, grade, and prioritized findings

Example 2: Collection Batch Evaluation

User says: "Audit all agents and skills" Actions:

1. List all agents/.md and skills//SKILL.md (IDENTIFY)
2. Run Steps 2-5 for each target (EVALUATE)
3. Generate individual reports + collection summary (REPORT) Result: Per-item scores plus distribution, top performers, and improvement areas

Example 3: V2 Migration Compliance Check

User says: "Check if systematic-refactoring skill meets v2 standards" Actions:

1. Confirm skills/systematic-refactoring/ exists (IDENTIFY)
2. Check YAML uses list allowed-tools, pipe description, version 2.0.0 (STRUCTURAL)
3. Verify Operator Context has correct item counts: Hardcoded 5-8, Default 5-8, Optional 3-5 (STRUCTURAL)
4. Confirm CAN/CANNOT sections, gates in Instructions, anti-rationalization table (STRUCTURAL)
5. Count total lines, run code checks (CONTENT + CODE)
6. Generate scored report highlighting v2 gaps (REPORT) Result: Report with specific v2 compliance gaps and required actions

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Error Handling

Error: "File Not Found"

Cause: Agent or skill path incorrect, or item was deleted Solution: Verify path exists with ls before evaluation. If truly missing, exclude from batch and note in report.

Error: "Cannot Parse YAML Front Matter"

Cause: Malformed YAML — missing --- delimiters, bad indentation, or invalid syntax Solution: Flag as HIGH priority structural failure. Score YAML section as 0/10. Include the specific parse error in the report.

Error: "Python Syntax Error in Script"

Cause: Validation script has syntax issues Solution: Run python3 -m py_compile and capture the specific error. Score validation script as 0/10. Include error output in report.

Error: "Operator Context Item Counts Out of Range"

Cause: v2 standard requires Hardcoded 5-8, Default 5-8, Optional 3-5 items. Skill has too few or too many. Solution:

1. Count actual items per behavior type (bold items starting with - **)
2. If too few: flag as MEDIUM priority — behaviors likely need to be split or added
3. If too many: flag as LOW priority — behaviors may need consolidation
4. Score Operator Context at partial credit (10/20) if counts are wrong

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References

Reference Files

• ${CLAUDE_SKILL_DIR}/references/scoring-rubric.md - Full/partial/no credit breakdowns per rubric category
• ${CLAUDE_SKILL_DIR}/references/report-templates.md - Standard report format templates (single, batch, comparison)
• ${CLAUDE_SKILL_DIR}/references/common-issues.md - Frequently found issues with fix templates
• ${CLAUDE_SKILL_DIR}/references/batch-evaluation.md - Batch evaluation procedures and collection summary format