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ClaudeWave
Skill329 repo starsupdated 5d ago

benchmark-audit

# ClaudeWave: benchmark-audit The benchmark-audit skill conducts systematic quality evaluations of AI/ML benchmarks using the BetterBench 46-criterion framework combined with Datasheets for Datasets standards and psychometric principles. Use this skill when you need to assess benchmark documentation completeness, construct validity, statistical robustness, maintenance status, and known failure modes across multiple benchmarks through structured analysis of papers, web searches, and documentation audits.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/benchmark-audit && cp -r /tmp/benchmark-audit/skills/benchmark-audit ~/.claude/skills/benchmark-audit
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# Benchmark Audit Strategy

Systematic quality assessment of AI/ML benchmarks using the BetterBench 46-criterion framework, Datasheets for Datasets standards, and established psychometric evaluation principles.

## Purpose

Produce a structured quality report for each target benchmark covering: documentation completeness, construct validity indicators, statistical robustness, maintenance status, and known failure modes.

## Budget

| Resource | Floor | Target |
|----------|-------|--------|
| Benchmarks audited | 3 | 5 |
| Papers read | 20 | 30 |
| Web searches | 25 | 40 |

## State Ledger

```
<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Benchmarks audited | 0 | 5 | PENDING |
| Papers fetched | 0 | 30 | PENDING |
| Papers read | 0 | 20 | PENDING |
| Web searches | 0 | 40 | PENDING |
| Documentation audits complete | 0 | 5 | PENDING |
| Metric decompositions complete | 0 | 5 | PENDING |
| Contamination checks complete | 0 | 5 | PENDING |
| Synthesis reports produced | 0 | 5 | PENDING |
</HARD-GATE>
```

Cannot exit until 80% of all targets met.

## Available Tactics

- **artifact-detection** — Probe for annotation artifacts and dataset shortcuts

## Available SOPs

- **benchmark-inventory** — Identify target benchmarks in domain
- **metric-decomposition** — Decompose composite metrics into constituent signals
- **contamination-audit** — Detect train-test data leakage
- **documentation-audit** — Assess documentation completeness (BetterBench/Datasheets)
- **benchmark-synthesis** — Produce final structured audit report

## Execution Guidance

1. **Inventory Phase**: Use benchmark-inventory to identify 5 benchmarks in target domain
2. **Per-Benchmark Loop** (repeat for each benchmark):
   a. Gather benchmark paper, documentation, leaderboard via web searches
   b. Run documentation-audit against BetterBench 46 criteria
   c. Run metric-decomposition on primary metric(s)
   d. Run contamination-audit checking known training corpora
   e. Run artifact-detection tactic if annotation-based benchmark
   f. Collect findings into per-benchmark report
3. **Synthesis Phase**: Run benchmark-synthesis to produce cross-benchmark comparison

## Output Format

```yaml
benchmark_audit:
  benchmark_name: string
  version: string
  betterbench_score: float  # 0-1, proportion of 46 criteria met
  documentation_grade: A|B|C|D|F
  metric_analysis:
    primary_metric: string
    ceiling_effects: boolean
    polarity_issues: list
  contamination_risk: low|medium|high|critical
  artifact_risk: low|medium|high
  maintenance_status: active|stale|abandoned
  key_findings: list[string]
  recommendations: list[string]
```