feature-store-and-ml-data-pipelines — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited feature-store-and-ml-data-pipelines (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 the platform must support model training and inference safely. It helps agents design feature generation, point-in-time correctness, serving parity, and operational contracts for ML-focused data products.
Do not treat feature pipelines as ordinary marts with different names. ML pipelines have different correctness risks.
Include:
Training data must only include information available at prediction time.
Reuse definitions and validation wherever possible to prevent training-serving drift.
Clarify:
Models break when stale or missing features silently propagate.
| Rationalization | Reality |
|---|---|
| "We can use the latest value for training." | That often introduces leakage and overstates model performance. |
| "Online parity is a model-team problem." | Feature consistency is a data pipeline responsibility too. |
| "Features are internal, so contracts are unnecessary." | Unclear feature meaning leads to misuse and drift. |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.