ml-feature-engineering
Ml Feature Engineering
Overview
Use this skill to design features that are useful, explainable, and consistent across training and serving.
Scope Boundaries
- Use this skill when the task matches the trigger condition described in
description. - Do not use this skill when the primary task falls outside this skill's domain.
Shared References
- Online/offline parity rules:
references/online-offline-parity-rules.md
Templates And Assets
- Feature specification template:
assets/feature-spec-template.csv
Inputs To Gather
- Candidate feature hypotheses and business rationale.
- Data sources and freshness constraints.
- Serving path capabilities and latency budget.
- Leakage/fairness/compliance constraints.
Deliverables
- Feature catalog with lineage and ownership.
- Parity validation plan for train vs serve paths.
- Feature risk and maintenance notes.
Workflow
- Define feature specs in
assets/feature-spec-template.csv. - Validate parity assumptions with
references/online-offline-parity-rules.md. - Prioritize features by incremental value vs complexity.
- Verify leakage and freshness assumptions.
- Publish feature rollout and deprecation plan.
Quality Standard
- Feature definitions are versioned and reproducible.
- Online/offline behavior is consistent for decision-critical features.
- Feature ownership and monitoring are explicit.
Failure Conditions
- Stop when feature logic diverges between training and serving.
- Stop when feature value cannot justify operational complexity.
- Escalate when parity gaps remain unresolved.
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