mikeparcewski/wicked-garden-data
skills/data/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add mikeparcewski/wicked-garden wicked-garden-dataInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- wicked-garden-data
- user-invocable:
- true
- description:
- |
- Use when:
- analyze this CSV/Excel/data file" / "explore this dataset" /
- public ontology". Replaces the former /wicked-garden:
- data:* commands
- # TODO #339:
- When Claude Code supports 'paths' in skill frontmatter for
- # paths:
- ["**/dags/**", "**/pipelines/**", "**/etl/**", "**/airflow/**", "**/prefect/**"]
- 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, defaultstats),--context,--refresh,--scenarios. - Read first rows of the file to capture column names / types / nulls / sample.
Read("${CLAUDE_PLUGIN_ROOT}/skills/data/refs/analyze.md")— the EDA rubric, quality/warehouse/ml modes, insight pattern, and output format.- Apply the rubric directly for the chosen
--focusmode and emit the analysis.
Profile / Validate / Quality — core data engineering ops
Schema-level engineering ops on a dataset. NOT for interactive exploration
(use analyze) or ML pipeline review (use ml).
- Parse the sub-action (profile|validate|quality) and its args (
<path>,--schemafor validate). - Read the data file head/tail to capture columns / types / nulls / sample.
Read("${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,--typefor pipeline). - For
review, read model files at<path>. Read("${CLAUDE_PLUGIN_ROOT}/skills/data/refs/ml.md")— the model review checklist, pipeline design template, deployment readiness checklist, and MLOps standards.- Apply the rubric for the requested sub-action and emit structured markdown.
Pipeline — data pipeline design and review
Data pipeline design and review. NOT for ML training pipelines (use
ml pipeline) or one-off file analysis (use analyze).
- Parse the sub-action (design|review) and args. For
review, read pipeline files at<path>. Fordesign, capture--source,--target,--frequency. Read("${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-pathfrom 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.
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