test-data-preparation-and-synthetic-data — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited test-data-preparation-and-synthetic-data (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill when data work needs realistic test inputs without depending on unsafe copies of production. It helps agents decide when to use masked subsets, synthetic data, seeded fixtures, or contract-shaped test datasets.
Do not assume production copies are the default answer for testing.
Clarify whether the data is needed for:
Decide between:
Include:
Make sure:
Record:
| Rationalization | Reality |
|---|---|
| "We need real data or the tests are useless." | Many test goals are satisfied by synthetic or masked data when the right shape and edge cases are preserved. |
| "A quick production copy is faster." | Unsafe lower-environment copies often become long-lived risk surfaces. |
| "The happy path sample is enough." | Test data that omits skew, nulls, duplicates, or boundary conditions gives false confidence. |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.