Test Data Generation

SkillDev tools

Use 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.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

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

Others that do the same job

test-data-generation
Skill · a5c-ai

Advanced

Item type: skill
Key: test-data-generation-charliehzm
Source: github.com/charliehzm/medharness

github.com/charliehzm/medharness