Test Data Generation
SkillDev toolsUse this skill at Step 5 of the v2 SOP to generate stage-isolated synthetic test fixtures (mock data) for an OpenSpec change. Outputs fixtures into mock/阶段N-<名称>/, computes fingerprints, declares synthetic-only source, and runs the fingerprint match against the real-sample fingerprint library. Chinese trigger examples: "生成测试数据", "Mock 数据生成", "Step 5", "合成 fixture", "生成 mock", "测试数据 generation". Do NOT use to sample from production, do NOT use real PHI as seed even after redaction. Success = fixtures generated, fingerprints computed, fingerprint match against real-sample library returns zero collisions, source declaration marked synthetic-only.
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 Test Data Generation skill
What this skill tells your AI
The instructions your AI receives, as published by charliehzm/medharness in .claude/skills/test-data-generation/SKILL.md and read by ahel’s review.
Generates fixtures the change can be tested against, with the strong invariant: never reversible to real patients.
Generation modes (pick one)
| Mode | When | Risk |
|---|---|---|
| Pure synthetic (faker-style) | Default | Low |
| Schema-driven random | When distribution matters less than shape | Low |
| Distribution-matched synthetic (DP) | When ML features depend on realistic distributions | Medium — guard against re-identification |
| Real → fully-decoupled (rare) | Only with Data Steward signoff + 18-id strip + outlier removal + k-anonymity ≥ 5 | High — extra audit |
Forbidden mode: "sample from production then mask names". Cannot prevent re-identification. Refuse.
Workflow
- Read schema from change specs (data model section in design.md).
- Choose generation mode; document choice in
source_declaration.md. - Generate per-stage directory:
mock/阶段N-<名称>/. - For each output file, compute fingerprints (sha256 of canonical-sorted rows).
- Compare fingerprints against the real-sample fingerprint library (held by Data Steward).
- If any collision/near-match: stop, escalate, do not write fixtures.
- Else: write fixtures + fingerprints.txt + source_declaration.md.
Source declaration template
# Source Declaration · 阶段N-<名称>
- 生成模式: <pure-synthetic / schema-driven / distribution-matched / real-decoupled>
- 生成时间: <ts>
- 生成器: <tool + version>
- 真实样本接触: 否（默认）/ 是（需 Data Steward 签字）
- 指纹核验: <PASS / FAIL>，对比库版本 <x.y>
- 合规等级承诺: 本数据集等同 L1，可在所有 allowlist 模型上使用
Common failure modes
- "Looks real" generators — Faker with locale 'zh_CN' produces plausible Chinese names but no link to real patients; this is fine. The trap is when devs seed with real values.
- Distribution-matched without DP — releasing means/variances can leak. Mitigation: differential privacy budget recorded in source declaration.
- Forgetting fingerprint match — fixtures written but never checked. Mitigation: this skill MUST run fingerprint compare before write.
- Cross-stage mixing — same fixture used across stages. Mitigation: per-stage directory; do not symlink.
Signals
- GitHub stars
- 86
- Forks
- 8
- Last commit
- Jun 2026
Advanced
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- skill
- Gateway key
test-data-generation- Source
- github.com/charliehzm/medharness