test-data-generation git:20261011.7d9502bA

Immutable. This exact content never changes and is served at /api/v1/blob/31257c741f195412.

---
name: test-data-generation
description: Use this skill when you need to design safe, representative test data from stated constraints; triggers include test data generation.
---

# Test Data Generation

## When to Use

- Use this skill when you need to design reusable test datasets covering business rules, boundaries, combinations, and privacy constraints.
- Use it to review an existing plan, result, or evidence set and produce actionable improvements.
- Use it when context is incomplete but a bounded first pass is still valuable.

## Output Format Options

- Default to Markdown for review, execution, and incremental refinement.
- When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
- For machine-consumed output, confirm the schema, enums, and required fields first.

## How to Use

1. Read and follow `prompts/test-data-generation.md`, including its input contract, execution rules, minimum coverage, and output order.
2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
3. Audit the input, then separate confirmed facts, working assumptions, and open questions.
4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.

## Reference Files

- When calibrating domain outcomes, valid controls, or evidence gaps, read [references/domain-decision-example.md](references/domain-decision-example.md) for sanitized input, per-item oracles, and reproducible calculations.

- Always read `prompts/test-data-generation.md`; it is the complete execution specification for this skill.
- For evaluation or regression, read `evals/eval.yaml` and the relevant cases under `evals/cases/`.
- Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them.

## Domain Execution Essentials

1. Derive groups from supplied schema, foreign keys, uniqueness, state relationships, and scenario oracles. Fix seed/generator version; missing rules remain UNKNOWN without invented fields or real identities.
2. Separate valid happy/boundary data from deliberate invalid groups. Name the violated constraint and expected allow/reject outcome while preserving other relationships; invalid rows cannot silently enter valid groups.
3. Deliver actual sanitized samples with uniqueness, foreign-key, type/range, state, and reproducibility checks. Equal seeds must reproduce data identities, not merely a strategy statement.
4. Use isolated IDs/namespaces with provenance, privacy restrictions, and reverse dependency cleanup. Missing authority prevents real personal-data collection; synthetic rows do not establish population representativeness.

## Core Constraints

- never use real personal sensitive data
- distinguish synthetic, masked, and production-like data
- generated data must be reproducible and cleanable
- Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
- Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
- Explain priority using business impact, likelihood, or detectability.

## Delivery Checklist

- [ ] Covered: valid and invalid values, boundaries, equivalence classes, combinations, referential integrity, states, roles, localization, privacy, cleanup.
- [ ] Separated facts, assumptions, gaps, and recommendations.
- [ ] Gave high-risk items a priority, evidence basis, owner or next action.
- [ ] Defined verifiable decision criteria instead of generic advice.
- [ ] Performed no unauthorized production writes or destructive actions.

## Common Pitfalls

- Listing checks without preconditions, expected outcomes, or evidence.
- Marking everything high priority and avoiding tradeoffs.
- Substituting tool names or generic theory for domain reasoning.
- Refusing incomplete input, or pretending incomplete evidence supports certainty.

## Best Practices

- Start with paths most likely to cause business loss, safety issues, or release blockage.
- Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
- Make the artifact executable and independently reviewable by another engineer.