| name | wicked-garden-data |
| user-invocable | true |
| description | Data engineering, analysis, and ML toolkit. One skill, routed sub-actions:
analyze (interactive EDA on a CSV/Excel/data file), profile / validate /
quality (schema-level data engineering ops), ml review / ml pipeline
(model review + training-pipeline design), pipeline design / pipeline
review (ETL architecture), and ontology (recommend Schema.org, Dublin
Core, DCAT, FOAF, GoodRelations, SKOS, or a custom shape for a dataset).
Use when: "analyze this CSV/Excel/data file" / "explore this dataset" /
one-off data exploration; "profile this dataset" / "validate data against
a schema" / "generate a data quality report" (completeness, uniqueness,
validity); "review this ML model" / "design an ML training pipeline";
"design a data pipeline" / "review this ETL" / "optimize data
processing"; "recommend an ontology for this data" / "map columns to a
public ontology". Replaces the former /wicked-garden:data:* commands
(analyze, data, ml, ontology, pipeline).
NOT for delegated subagent work — dispatch the wicked-garden-data-engineer
fork skill for that.
|
| phase_relevance | ["clarify","design","build"] |
| archetype_relevance | ["*"] |
wicked-garden:data — data engineering, analysis, and ML
Every sub-action runs inline (no dispatch): 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, dispatch the wicked-garden-data-engineer fork
skill instead.
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).
- Parse
<file-path>, --focus (stats|quality|warehouse|ml, default
stats), --context, --refresh, --scenarios.
- Read first rows of the file to capture column names / types / nulls / sample.
Read("${CLAUDE_PLUGIN_ROOT}/skills/data/refs/analyze.md") — the EDA
rubric, quality/warehouse/ml modes, insight pattern, and output format.- Apply the rubric directly for the chosen
--focus mode and emit the analysis.
Profile / Validate / Quality — core data engineering ops
Schema-level engineering ops on a dataset. NOT for interactive exploration
(use analyze) or ML pipeline review (use ml).
- Parse the sub-action (profile|validate|quality) and its args (
<path>,
--schema for validate).
- Read the data file head/tail to capture columns / types / nulls / sample.
Read("${CLAUDE_PLUGIN_ROOT}/skills/data/refs/data.md") — the profile,
validate, and quality rubrics with output formats and quality thresholds.- Apply the rubric for the requested sub-action and emit structured markdown
with tables and prioritized findings.
Optional scripted paths (deterministic profiling/validation):
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/scripts/data/data_profiler.py" \
--input data.csv --output profile.json
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/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).
- Parse the sub-action (review|pipeline) and args (
<path> for review,
--type for pipeline).
- For
review, read model files at <path>.
Read("${CLAUDE_PLUGIN_ROOT}/skills/data/refs/ml.md") — the model review
checklist, pipeline design template, deployment readiness checklist, and
MLOps standards.- Apply the rubric for the requested sub-action and emit structured markdown.
Pipeline — data pipeline design and review
Data pipeline design and review. NOT for ML training pipelines (use
ml pipeline) or one-off file analysis (use analyze).
- Parse the sub-action (design|review) and args. For
review, read pipeline
files at <path>. For design, capture --source, --target,
--frequency.
Read("${CLAUDE_PLUGIN_ROOT}/skills/data/refs/pipeline.md") — the design
checklist, review rubric with P1/P2/P3 findings, pattern selection, and
engineering standards.- 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).
-
Arg parse — extract file-path from the arguments.
-
Run recommender:
cd "${CLAUDE_PLUGIN_ROOT}" && uv run python scripts/_run.py scripts/data/ontology_recommender.py "${file_path}"
-
Present the script's match table, column-mapping suggestions, and any
custom-ontology fallback inline.
