skill-tester v1.0.0A

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---
name: skill-tester
description: >
  Validate and score Claude Code skill packages for quality, completeness, and best-practice
  compliance. Tests Python scripts, checks YAML frontmatter, and generates reports. Use when
  creating, validating, or auditing skill packages.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: engineering
  domain: meta-skills
  tier: POWERFUL
  updated: 2026-03-31
---
# Skill Tester

The agent validates skill packages for structure compliance, tests Python scripts for syntax and stdlib-only imports, and scores quality across four dimensions (documentation, code quality, completeness, usability) with letter grades and improvement recommendations. It supports BASIC, STANDARD, and POWERFUL tier classification.

## Quick Start

```bash
# Validate skill structure and documentation
python skill_validator.py engineering/my-skill --tier POWERFUL --json

# Test all Python scripts in a skill
python script_tester.py engineering/my-skill --timeout 30

# Score quality with improvement roadmap
python quality_scorer.py engineering/my-skill --detailed --minimum-score 75
```

---

## Core Workflows

### Workflow 1: Validate a New Skill

1. Run `skill_validator.py` with target tier to check structure, frontmatter, required sections, and scripts
2. Review errors (blocking) and warnings (non-blocking) in the report
3. Fix all errors -- missing SKILL.md, invalid frontmatter, external imports
4. **Validation checkpoint:** Score >= 60; zero errors; all scripts pass `ast.parse()`

```bash
python skill_validator.py engineering/my-skill --tier STANDARD --json
```

### Workflow 2: Test Skill Scripts

1. Run `script_tester.py` to execute syntax validation, import analysis, and runtime tests
2. Review per-script results: argparse detection, `--help` output, sample data execution
3. Fix failures: add `if __name__ == "__main__"` guards, replace external imports with stdlib
4. **Validation checkpoint:** All scripts pass syntax; zero external imports; `--help` exits cleanly

```bash
python script_tester.py engineering/my-skill --timeout 60 --json
```

### Workflow 3: Score and Improve Quality

1. Run `quality_scorer.py` with `--detailed` for component-level breakdowns
2. Review the prioritized improvement roadmap (up to 5 items)
3. Address HIGH-priority items first (documentation gaps, missing error handling)
4. Re-run to verify score improvement
5. **Validation checkpoint:** Overall score >= 75; no dimension below 50%

```bash
python quality_scorer.py engineering/my-skill --detailed --minimum-score 75 --json
```

---

## Tier Requirements

| Requirement | BASIC | STANDARD | POWERFUL |
|-------------|-------|----------|----------|
| SKILL.md lines | 100+ | 200+ | 300+ |
| Python scripts | 1 (100-300 LOC) | 1-2 (300-500 LOC) | 2-3 (500-800 LOC) |
| Argparse | Basic | Subcommands | Multiple modes |
| Output formats | Single | JSON + text | JSON + text + validation |
| Error handling | Essential | Comprehensive | Advanced recovery |

---

## Quality Scoring Dimensions

| Dimension | Weight | Measures |
|-----------|--------|----------|
| Documentation | 25% | SKILL.md depth, README clarity, reference quality |
| Code Quality | 25% | Complexity, error handling, output consistency |
| Completeness | 25% | Required files, sample data, expected outputs |
| Usability | 25% | Argparse help text, example clarity, ease of setup |

**Grades:** A+ (97+) through F (<40). Exit code 0 for A+ through C-, exit code 2 for D, exit code 1 for F.

---

## CI/CD Integration

```yaml
# GitHub Actions example
- name: Validate Changed Skills
  run: |
    for skill in $(git diff --name-only | grep -E '^engineering/[^/]+/' | cut -d'/' -f1-2 | sort -u); do
      python engineering/skill-tester/scripts/skill_validator.py $skill --json
      python engineering/skill-tester/scripts/script_tester.py $skill
      python engineering/skill-tester/scripts/quality_scorer.py $skill --minimum-score 75
    done
```

---

## Anti-Patterns

- **Padding SKILL.md with filler** -- line count thresholds measure substantive content; blank lines and boilerplate do not count
- **External imports disguised as stdlib** -- the import allowlist is manually maintained; if a legit stdlib module is flagged, add it to `stdlib_modules`
- **Missing argparse help strings** -- usability scoring requires `help=` parameters on every argument; empty help strings score zero
- **No `__main__` guard** -- scripts without `if __name__ == "__main__"` fail runtime tests when imported
- **Relying on SKILL.md for usability** -- usability is scored from scripts and README independently; a detailed SKILL.md does not compensate for missing `--help` output

## Troubleshooting

| Problem | Cause | Solution |
|---------|-------|----------|
| `SKILL.md too short` error despite sufficient content | Validator counts only non-blank lines; blank lines inflate raw line count but are excluded from the tally | Remove excessive blank lines or add more substantive content sections to meet the tier threshold |
| YAML frontmatter parse failure | Frontmatter contains invalid YAML syntax (unquoted colons, tabs instead of spaces, missing closing `---`) | Validate frontmatter through `yaml.safe_load()` locally; ensure the closing `---` marker is present on its own line |
| External import false positive | The stdlib module allowlist in `skill_validator.py` and `script_tester.py` is manually maintained and may not include every standard library module | Add the missing module name to the `stdlib_modules` set in the relevant script, or restructure the import |
| Script execution timeout during testing | Script requires interactive input, enters an infinite loop, or performs long-running computation | Increase `--timeout` value, add early-exit logic for missing arguments, or ensure scripts exit cleanly when no input is provided |
| Tier compliance check fails despite passing individual checks | `_validate_tier_compliance` only examines `skill_md_exists`, `min_scripts_count`, and `skill_md_length`; other failures (e.g., missing directories) are reported separately | Fix the specific critical checks listed in the error message; review the `TIER_REQUIREMENTS` dictionary for the target tier |
| Quality scorer reports low usability despite good documentation | Usability dimension scores help text inside scripts, `README.md` usage sections, and practical example files independently of SKILL.md content | Add `argparse` help strings with `help=` parameters, include a `Usage` section in README.md, and place sample/example files in the `assets/` directory |
| `--json` flag produces no output | Script raised an unhandled exception before reaching the output formatter; errors are written to stderr | Run with `--verbose` to see the full traceback on stderr, then address the underlying exception |

## Success Criteria

- **Structure pass rate above 95%**: Validated skills pass all required-file and directory-structure checks on first run in at least 95% of cases.
- **Script syntax zero-defect**: Every Python script in a validated skill compiles without `SyntaxError` via `ast.parse()`.
- **Standard library compliance 100%**: No external (non-stdlib) imports detected across all validated scripts.
- **Quality score consistency within 5 points**: Re-running `quality_scorer.py` on an unchanged skill produces scores that vary by no more than 5 points across runs.
- **Execution time under 10 seconds per skill**: Full validation, testing, and scoring pipeline completes in under 10 seconds for a single skill with up to 3 scripts.
- **Actionable recommendation density**: Every skill scoring below 75/100 receives at least 3 prioritized improvement suggestions in the roadmap.
- **CI/CD gate reliability**: When integrated as a GitHub Actions step, the tool exits with non-zero status for every skill that fails critical checks, blocking the merge.

## Scope & Limitations

**Covers:**
- Structural validation of skill directories against tier-specific requirements (BASIC, STANDARD, POWERFUL)
- Static analysis of Python scripts including syntax checking, import validation, argparse detection, and main guard verification
- Multi-dimensional quality scoring across documentation, code quality, completeness, and usability
- Dual output formatting (JSON for CI/CD pipelines, human-readable for developer consumption)

**Does NOT cover:**
- Functional correctness of script logic or algorithm accuracy — the tester verifies structure and conventions, not business logic
- Performance benchmarking or memory profiling of scripts — see `engineering/performance-profiler` for runtime analysis
- Security vulnerability scanning of script code — see `engineering/skill-security-auditor` for dependency and code security audits
- Cross-skill dependency resolution or integration testing — skills are validated in isolation without verifying inter-skill compatibility

## Integration Points

| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| `engineering/skill-security-auditor` | Run security audit after validation passes | `skill_validator.py` confirms structure compliance, then `skill-security-auditor` scans for vulnerabilities in the same skill path |
| `engineering/ci-cd-pipeline-builder` | Embed skill-tester as a quality gate stage | Pipeline builder generates workflow YAML that invokes `skill_validator.py`, `script_tester.py`, and `quality_scorer.py` sequentially |
| `engineering/changelog-generator` | Feed quality score deltas into changelog entries | Compare `quality_scorer.py` JSON output between releases to surface quality improvements or regressions |
| `engineering/pr-review-expert` | Attach validation report to pull request reviews | `skill_validator.py --json` output is posted as a PR comment for reviewer context |
| `engineering/performance-profiler` | Complement structural testing with runtime profiling | After `script_tester.py` confirms execution succeeds, `performance-profiler` measures execution time and resource usage |
| `engineering/tech-debt-tracker` | Track quality score trends over time | Periodic `quality_scorer.py --json` output is ingested to detect score degradation and flag technical debt |

## Tool Reference

### skill_validator.py

**Purpose:** Validates a skill directory's structure, documentation, and Python scripts against the claude-skills ecosystem standards. Checks required files, YAML frontmatter, required SKILL.md sections, directory layout, script syntax, import compliance, and tier-specific requirements.

**Usage:**
```bash
python skill_validator.py <skill_path> [--tier TIER] [--json] [--verbose]
```

**Parameters:**

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `skill_path` | positional | Yes | — | Path to the skill directory to validate |
| `--tier` | option | No | None | Target tier for validation: `BASIC`, `STANDARD`, or `POWERFUL` |
| `--json` | flag | No | Off | Output results in JSON format instead of human-readable text |
| `--verbose` | flag | No | Off | Enable verbose logging to stderr |

**Example:**
```bash
python skill_validator.py engineering/my-skill --tier POWERFUL --json
```

**Output Formats:**
- **Human-readable (default):** Grouped report with STRUCTURE VALIDATION, SCRIPT VALIDATION, ERRORS, WARNINGS, and SUGGESTIONS sections. Displays overall score out of 100 with compliance level (EXCELLENT, GOOD, ACCEPTABLE, NEEDS_IMPROVEMENT, POOR).
- **JSON (`--json`):** Object with keys `skill_path`, `timestamp`, `overall_score`, `compliance_level`, `checks` (dict of check name to pass/message/score), `warnings`, `errors`, `suggestions`.

**Exit codes:** `0` on success (score >= 60 and no errors), `1` on failure.

---

### script_tester.py

**Purpose:** Tests all Python scripts within a skill's `scripts/` directory. Performs syntax validation via AST parsing, import analysis for stdlib compliance, argparse implementation verification, main guard detection, runtime execution with timeout protection, `--help` functionality testing, sample data processing against files in `assets/`, and output format compliance checks.

**Usage:**
```bash
python script_tester.py <skill_path> [--timeout SECONDS] [--json] [--verbose]
```

**Parameters:**

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `skill_path` | positional | Yes | — | Path to the skill directory containing scripts to test |
| `--timeout` | option | No | `30` | Timeout in seconds for each script execution test |
| `--json` | flag | No | Off | Output results in JSON format instead of human-readable text |
| `--verbose` | flag | No | Off | Enable verbose logging to stderr |

**Example:**
```bash
python script_tester.py engineering/my-skill --timeout 60 --json
```

**Output Formats:**
- **Human-readable (default):** Report with SUMMARY (total/passed/partial/failed counts), GLOBAL ERRORS, and per-script sections showing status, execution time, individual test results, errors, and warnings.
- **JSON (`--json`):** Object with keys `skill_path`, `timestamp`, `summary` (counts and overall status), `global_errors`, `script_results` (dict per script with `overall_status`, `execution_time`, `tests`, `errors`, `warnings`).

**Exit codes:** `0` on full success, `1` on failure or global errors, `2` on partial success.

---

### quality_scorer.py

**Purpose:** Provides a comprehensive multi-dimensional quality assessment for a skill. Evaluates four equally weighted dimensions — Documentation (25%), Code Quality (25%), Completeness (25%), and Usability (25%) — and produces an overall score, letter grade (A+ through F), tier recommendation, and a prioritized improvement roadmap.

**Usage:**
```bash
python quality_scorer.py <skill_path> [--detailed] [--minimum-score SCORE] [--json] [--verbose]
```

**Parameters:**

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `skill_path` | positional | Yes | — | Path to the skill directory to assess |
| `--detailed` | flag | No | Off | Show detailed component scores within each dimension |
| `--minimum-score` | option | No | `0` | Minimum acceptable overall score; exits with error code `1` if the score falls below this threshold |
| `--json` | flag | No | Off | Output results in JSON format instead of human-readable text |
| `--verbose` | flag | No | Off | Enable verbose logging to stderr |

**Example:**
```bash
python quality_scorer.py engineering/my-skill --detailed --minimum-score 75 --json
```

**Output Formats:**
- **Human-readable (default):** Report with overall score and letter grade, per-dimension scores with weights, summary statistics (highest/lowest dimension, dimensions above 70%, dimensions below 50%), and a prioritized improvement roadmap (up to 5 items with HIGH/MEDIUM/LOW priority). When `--detailed` is used, component-level breakdowns appear under each dimension.
- **JSON (`--json`):** Object with keys `skill_path`, `timestamp`, `overall_score`, `letter_grade`, `tier_recommendation`, `summary_stats`, `dimensions` (per-dimension name/weight/score/details/suggestions), `improvement_roadmap` (list of priority/dimension/suggestion/current_score objects).

**Exit codes:** `0` for grades A+ through C-, `1` for grade F or when score is below `--minimum-score`, `2` for grade D.