Adding a Feature Operator

Audience: Pulse internals contributors adding a new FEAT_* operator — a pre-filter feature engineer that runs before the aggregation / window pass and emits one or more derived columns (FEAT_LOG, FEAT_SQRT, FEAT_BUCKETIZE, …).

The recipe mirrors the aggregator recipe; the feature-specific moving parts are the feature.StreamingComputer interface, the output-label emitter, and the predict-side label projection.

1. Declare the type constant

Add the new constant to types/types.go and the slice returned by types.AllFeatureTypes():

const (
    // ... existing constants ...
    FEAT_BOX_COX FeatureType = "FEAT_BOX_COX"
)

func AllFeatureTypes() []FeatureType {
    return []FeatureType{
        // ... existing entries, alphabetised ...
        FEAT_BOX_COX,
    }
}

2. Implement in processing/feature/

Each feature operator lives in processing/feature/<name>.go. Register via the package’s init() calling register(types.FEAT_X, newX).

If the operator is streaming-eligible, implement the feature.StreamingComputer interface — a three-method shape:

• PrePass(rows) — accumulate any whole-cohort statistics needed (mean, stddev) on a first pass.
• Finalize() — close out the pre-pass, compute coefficients.
• EmitRow(row) — emit the per-row feature value(s) on the second pass.

Operators without whole-cohort statistics skip PrePass and run as single-pass row transforms.

3. Tests

Write tests in processing/feature/<name>_test.go before the implementation. Cover the empty-input, single-row, null-bearing, and boundary cases.

4. Capability declaration

Add a row to descriptor/capabilities_features.go with the operator’s params, accepted field types, and any emit shape. TestManifestOperatorsComplete enforces a row per registered feature.

5. Predict-side label projection

Update descriptor/predict_feature.go:

• Validate the operator’s params (raise the appropriate PROCESSING_CONFIG / SERVICE_VALIDATION error code on invalid input).
• Emit the operator’s output column labels in featureOutputLabels so predict can show the LLM client what columns the request will materialise. TestPredict_Feature enforces parity.

6. Update the feature-engineering skill

Add a section in skills/feature-engineering.md covering the operator’s params and output column naming convention. The TestSkillsCoverAllComponents gate enforces presence by name.

7. Update CLAUDE.md

Bump the registered-feature count in CLAUDE.md’s “Skill Pack” section.

8. Run the gates

go test ./skills/ -run TestSkillsCoverAllComponents
go test ./descriptor/ -run 'TestManifestOperatorsComplete|TestPredict_Feature'
go test ./processing/feature/...

The Update Demand row for feature operators covers all of these in one PR; see The Update Demand.