Data Domain — data engineering, analysis, and ML

Every sub-action runs inline: parse the arguments, pre-read the data where noted,
load the Tier-3 rubric from refs/, apply it, and emit structured markdown with tables
and prioritized findings. For delegated or parallel worker execution, hand off to the
wicked-garden-data-engineer worker skill instead.

Hand-off — open the wicked-garden-data-engineer skill with the sub-action and its arguments as the argument; on Claude Code this is the Skill tool, on any other seat open the named skill from your catalog and carry it out inline, then continue here.

Runtime

Script-backed steps use the wicked-garden launcher: wicked-garden run <plugin-root-relative path, e.g. scripts/…> [args]. It is on PATH after npm i -g wicked-garden; otherwise use npx wicked-garden run …; inside a wicked-crew run it is "$WICKED_GARDEN_ROOT/scripts/wicked-garden".
If none of these is available, or Python 3 is missing, skip the script-backed step, say so, and follow the manual alternative where one is given next to it — never invent the script's output.
Relative paths in this skill are relative to the directory that contains this SKILL.md.

Routing

Sub-action	Use for	Rubric
analyze <file-path> [--focus stats|quality|warehouse|ml] [--context <file>] [--refresh] [--scenarios]	one-off exploration of a CSV/Excel/data file	refs/analyze.md
profile <path>	dataset structure + quality profile	refs/data.md
validate --schema <schema> --data <path>	schema validation	refs/data.md
quality <path>	quality report (completeness, uniqueness, validity)	refs/data.md
ml review <path>	ML model review	refs/ml.md
ml pipeline --type <classification|regression|ranking>	training-pipeline design	refs/ml.md
pipeline design --source <src> --target <tgt> [--frequency <freq>]	ETL pipeline design	refs/pipeline.md
pipeline review <path>	ETL pipeline review	refs/pipeline.md
ontology <file-path>	ontology recommendation for a dataset	inline (§ Ontology)

Detailed templates and examples: refs/analysis-templates.md,
refs/ml-templates.md,
refs/pipeline-templates.md,
refs/data-examples.md.

Analyze — interactive data analysis

Interactive analysis on a CSV/Excel/data file. Use for one-off data
exploration. NOT for schema-level checks (use profile / validate /
quality) or pipeline review (use pipeline review).

1. Parse <file-path>, --focus (stats|quality|warehouse|ml, default
   stats), --context, --refresh, --scenarios.
2. Read first rows of the file to capture column names / types / nulls / sample.
3. Read refs/analyze.md (relative to this skill's base directory) — the EDA
   rubric, quality/warehouse/ml modes, insight pattern, and output format.
4. Apply the rubric directly for the chosen --focus mode and emit the analysis.

Profile / Validate / Quality — core data engineering ops

Schema-level engineering ops on a dataset. NOT for interactive exploration
(use analyze) or ML pipeline review (use ml).

1. Parse the sub-action (profile|validate|quality) and its args (<path>,
   --schema for validate).
2. Read the data file head/tail to capture columns / types / nulls / sample.
3. Read refs/data.md — the profile,
   validate, and quality rubrics with output formats and quality thresholds.
4. Apply the rubric for the requested sub-action and emit structured markdown
   with tables and prioritized findings.

Optional scripted paths (deterministic profiling/validation):

wicked-garden run scripts/data/data_profiler.py \
  --input data.csv --output profile.json

wicked-garden run scripts/data/schema_validator.py \
  --schema schemas/expected.json \
  --data data/actual.csv

For files >1GB, use the analyze sub-action for efficient SQL-based profiling
via DuckDB.

ML — model review and training-pipeline design

ML model review and training-pipeline design. NOT for ETL pipeline design
(use pipeline) or data profiling (use profile).

1. Parse the sub-action (review|pipeline) and args (<path> for review,
   --type for pipeline).
2. For review, read model files at <path>.
3. Read refs/ml.md — the model review
   checklist, pipeline design template, deployment readiness checklist, and
   MLOps standards.
4. Apply the rubric for the requested sub-action and emit structured markdown.

Pipeline — data pipeline design and review

Data pipeline design and review. NOT for ML training pipelines (use
ml pipeline) or one-off file analysis (use analyze).

1. Parse the sub-action (design|review) and args. For review, read pipeline
   files at <path>. For design, capture --source, --target,
   --frequency.
2. Read refs/pipeline.md — the design
   checklist, review rubric with P1/P2/P3 findings, pattern selection, and
   engineering standards.
3. Apply the rubric for the requested sub-action and emit structured markdown.

Ontology — recommend ontologies for a dataset

Sample a dataset (CSV/Excel/Parquet/JSON) and recommend matching public
ontologies (Schema.org, Dublin Core, DCAT, FOAF, GoodRelations, SKOS) or a
custom shape. Use this for ontology mapping. NOT for interactive analysis
(use analyze) or quality reports (use quality).

1. Arg parse — extract file-path from the arguments.

2. Run recommender:

   wicked-garden run scripts/_run.py scripts/data/ontology_recommender.py "${file_path}"

3. Present the script's match table, column-mapping suggestions, and any
   custom-ontology fallback inline.