Ml feature engineering
Skill planifest/planifest-framework/planifest-framework/external-skills/ml-feature-engineering
ML feature engineering workflow for feature definition, lineage, and online-offline parity. Use when model performance depends on explicit feature design and parity controls; do not use for generic API-layer or infrastructure-only changes.From its SKILL.md
npx -y skills add planifest/planifest-framework --skill ml-feature-engineeringAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.9 KB, 315 tokens by cl100k_base, as published. Nobody here has run it
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.
What ships with it: 1 file
11.3 KB alongside SKILL.md
- attribution.txt11.3 KB