Data

Skill mikeparcewski/wicked-garden/skills/data

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.From its SKILL.md

Install

npx -y skills add mikeparcewski/wicked-garden --skill data

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

6.6 KB, as published. Nobody here has run it

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.