# Feature Engineer > Explores data sources and engineers leakage-safe model features from files or databases. Use when asked to create features from raw tables/datasets, design joins and aggregations for a target label, improve model signal, or map business outcomes into trainable predictor sets. Invoke this automatically whenever the user mentions raw data, tables, columns, or wants to build/improve a model — even if they don't say "feature engineering" explicitly. Always run this before proceeding to pipeline validation or training when there is no established feature contract. - Skill: `lawwu/feature-engineer` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add lawwu/feature-engineer` - Raw SKILL.md: https://api.skillmd.com/api/skills/lawwu/feature-engineer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: lawwu (https://skillmd.com/u/lawwu) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/lawwu/feature-engineer --- # Feature Engineer Explore source data and build reproducible, leakage-safe feature sets for the stated label and business outcome. ## Invocation Arguments (`$ARGUMENTS`) are interpreted as: - `--data PATH|DATASET` — file-based dataset or dataset identifier - `--db-url DSN` — database connection string for SQL exploration - `--tables T1,T2,...` — candidate source tables for feature generation - `--label-col COLUMN` — target column - `--entity-col COLUMN` — entity key for joining and split safety - `--timestamp-col COLUMN` — event timestamp for point-in-time joins - `--outcome TEXT` — business outcome statement - `--task` — modeling task type - `--out-dir DIR` — output directory for feature spec and implementation snippets Target: `$ARGUMENTS` ## Your responsibilities ### 1. Build the feature contract Define: - target label and business outcome - prediction unit and prediction time - allowed feature freshness window - prohibited leakage sources If this is missing, request the smallest clarification and continue. ### 2. Explore and profile feature sources For file-based data: - inspect schema, missingness, uniqueness, and basic distributions - identify candidate numerical, categorical, text, and timestamp columns For databases (`--db-url` + `--tables`): - inspect schemas, primary/foreign key candidates, and joinability - profile row counts, null rates, cardinality, and label coverage per table - validate entity/time coverage before building joins Use [references/db-feature-playbook.md](references/db-feature-playbook.md) for DB profiling and as-of join patterns. ### 3. Generate candidate features Use patterns from [references/feature-patterns.md](references/feature-patterns.md): - numeric transforms and clipping - categorical handling (frequency, target-safe encoding, hashing) - temporal features (recency, frequency, rolling stats) - cross-table aggregations by entity/window - text-derived and interaction features where relevant Tie each feature family to an explicit modeling hypothesis. ### 4. Enforce leakage safety Before finalizing features: - verify `feature_time <= prediction_time` - reject post-outcome columns - ensure train/validation/test splits are entity-safe and time-consistent - mark any risky feature as blocked with rationale ### 5. Produce implementation-ready outputs Deliver: - ranked feature inventory with rationale and risk - SQL or Python snippets to materialize features - data-quality checks required before training - a minimal feature set for first baseline model ### 6. Hand off to pipeline validation After feature plan is drafted: - instruct running `check-data-pipeline` against the engineered feature pipeline - include exact next commands for validation and training handoff ## Output format ```text Feature Engineering Brief ========================= Business outcome: <...> Label: <...> Entity: <...> Prediction time: <...> Source exploration: 1) | rows=<...> | usable keys=<...> | notes=<...> Candidate feature sets: 1) | hypothesis=<...> | leakage risk= Blocked features: 1) | reason= Initial baseline feature set: - Implementation snippets: - Decision: GO | NO-GO | CONDITIONAL Confidence: high|medium|low Next commands: - - ``` `GO`: feature contract complete, no blockers. `CONDITIONAL`: feature brief is complete but one or more features carry medium/high leakage risk that should be tracked. `NO-GO`: critical source information is missing or blockers prevent building a valid feature set. ### JSON artifact Write `feature-engineer.json` to `--out-dir` (or `./` if invoked standalone) following the schema in [../../references/schemas.md](../../references/schemas.md). Use vocabulary from [../../references/vocabulary.md](../../references/vocabulary.md). Key fields to populate: - `decision`: `GO` / `NO-GO` / `CONDITIONAL` - `sources_explored`, `feature_sets`, `blocked_features`, `baseline_feature_set` - `findings`: one entry per blocked feature (severity based on risk level) and any structural gaps ## Quick heuristics - No timestamp column → default to random split; warn that temporal leakage cannot be verified - High cardinality categoricals (> 500 unique values) → use frequency or hash encoding, not one-hot - Entity column with duplicates in training data → group-aware split is mandatory or evaluation will be inflated - Target-correlated ID column (e.g. `user_tier`, `account_type` that encodes the label) → flag as high leakage risk - Rolling aggregations without `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` on ordered window → silently leaks future data - Feature missingness > 30% in production but < 5% in training data → distribution shift blocker; check source query ## Stop conditions Stop when: - feature brief and implementation snippets are complete, or - critical source information (keys, timestamps, label definition) is missing and explicitly requested. ## Additional resources - [references/db-feature-playbook.md](references/db-feature-playbook.md) — DB exploration and as-of join templates - [references/feature-patterns.md](references/feature-patterns.md) — reusable feature families and guardrails