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SkillsMP has collected 32 skills from danielrosehill/Claude-Data-Wrangler-plugin. Open a skill to review its source and details.
Showing 32 of 32 collected skills.
Audit and standardise a dataset's header row against a naming convention (snake_case, camelCase, Title Case, kebab-case) and verify consistency with an existing or forthcoming data dictionary. Use when preparing a dataset for SQL loading, publishing, or when…
Produce localised versions of a dataset and/or its data dictionary with translated column headers (and optionally translated dictionary descriptions) so the same underlying data can be analysed by speakers of different languages. Use when the user wants…
Audit numeric columns for inconsistent decimal precision (e.g. some values at 4 dp, others at 2 dp) and round all values to a user-chosen precision. Use before SQL load or publishing when mixed precision would otherwise produce awkward `NUMERIC(x, y)` choices…
Generate a polished PDF document from a dataset using Typst, with layout chosen to match the data shape (wide tables → landscape multi-page reference, narrow tables → portrait report, per-record → one-record-per-page card/profile layout, grouped → sectioned…
Reconcile a canonical upstream data file with a downstream project that has diverged — the downstream has added enrichments, renamed columns, changed types, or restructured the data, so fresh upstream rows can't be loaded incrementally without transformation.…
Convert tabular geodata (CSV / Excel / Parquet) into GeoJSON (or GeoJSON Seq / newline-delimited GeoJSON) — inferring geometry from lat/lon columns, WKT/WKB columns, or address columns via geocoding. Handles CRS reprojection (default WGS84 / EPSG:4326),…
Add or update a CHANGELOG.md in a data repository, recording dataset versions, schema changes, row-count deltas, enrichments applied, and re-publications. Follows Keep-a-Changelog conventions adapted for datasets. Use when the user wants versioned…
Prepare or refactor a dataset for upload into a REST API or MCP server — mapping dataset columns to API request fields, handling batching, pagination, rate limits, authentication, idempotency, and error retries. Works from an OpenAPI spec the user provides, a…
Scan one or more flat data files (CSV, Parquet, JSON, JSONL, Excel) to assess data cleanliness and identify columns likely to fail SQL ingestion — inconsistent types, mixed delimiters, malformed dates, nullability mismatches, duplicate keys, encoding issues,…
Analyse two or more datasets and suggest cleaning strategies that would make them comparable — aligning divergent header/column names, reconciling type mismatches (string vs int vs float), unifying unit conventions, and harmonising categorical value…
Export an existing data dictionary to a polished PDF using Typst, with a branded title page, column reference table, provenance log, and known-issues section. Use when the user wants a shareable, printable, or client-facing version of a dataset's…
Suggest and explore potential enrichment approaches for a dataset — identifying fields that could be augmented via public reference data, derived calculations, geospatial lookup, temporal decomposition, or third-party APIs. Use when the user wants ideas for…
Advise on how to reshape a dataset for logical storage in a database — normalisation decisions, splitting denormalised rows into related tables, extracting repeating groups, separating dimensions from facts, promoting nested structures to joinable tables, and…
Analyse the user's dataset (structure, volume, relationships, query patterns, access latency needs) and recommend the most suitable database system — relational (Postgres, MySQL, SQLite), analytical (DuckDB, ClickHouse, BigQuery), document (MongoDB),…
Perform date/time format transformations on a dataset — converting between ISO 8601, epoch (seconds/millis), with-timezone, without-timezone, date-only, datetime, Unix timestamp, locale-specific display formats, and fiscal / Julian / week-number…
Transform existing tabular or JSON data into a graph-suitable representation (nodes, edges, properties) and emit it for a graph database (Neo4j, ArangoDB, Memgraph, Postgres + Apache AGE). Identifies candidate node types and edge relationships, produces…
Scan a dataset for columns whose values could be standardised to an ISO standard (countries → ISO 3166, currencies → ISO 4217, languages → ISO 639, dates → ISO 8601, subdivisions → ISO 3166-2, units → ISO 80000, MIME → IANA, etc.). Reports non-compliance,…
Scan a dataset for personally identifiable information (PII) — names, emails, phone numbers, addresses, government IDs, credit cards, IPs, dates of birth, geocoordinates — and produce a cell-level report of where PII was detected, with confidence scores and…
Load a flat dataset (CSV / Parquet / JSON / Excel) into a SQL database. Either uses an existing configured database connection or walks the user through configuring a new one (PostgreSQL, MySQL, SQLite, MSSQL, DuckDB). Creates the table if absent, validates…
Replace PII (or other sensitive values) in a dataset with synthetic but realistic substitutes, preserving statistical shape, formats, and referential integrity where needed. Use after pii-flag has identified sensitive cells and the user wants the dataset…
Assess whether a dataset uses a consistent Unicode character set and normalisation form across its text columns. Detects mixed scripts, mixed normalisation forms (NFC/NFD/NFKC/NFKD), mojibake, mixed encodings, zero-width characters, confusables (homoglyphs),…
Build a pipeline that takes the current working dataset, embeds the relevant text/fields, and upserts into a configured vector database backend (Pinecone, Qdrant, Weaviate, Milvus, pgvector, ChromaDB). Handles embedding model selection, chunking for long…
Create a data dictionary for a dataset (CSV, JSON, JSONL, Parquet, Excel) that documents every column/field — name, type, description, units, example values, nulls allowed, source. Use when a dataset has no accompanying documentation and the user wants one…
Add ISO 3166 country codes (alpha-2, alpha-3, numeric) to a dataset that references countries by name but lacks standardised codes. Use when the user has a CSV/JSON/Parquet/Excel dataset with country names and wants ISO 3166 codes added as new columns/fields.
Convert between CSV and JSON formats — CSV to JSON array, CSV to JSONL, JSON to CSV, JSONL to CSV. Handles type inference, header/record mapping, nested structure flattening, and encoding issues. Use when the user wants to reformat tabular data between…
Add ISO 4217 currency codes to a dataset by direct mapping from ISO 3166 country codes. Use when the dataset already has country codes and the user wants the local currency code (and optionally currency name/symbol) appended.
Push a prepared dataset to Hugging Face Hub as a Dataset repository, with dataset card (README.md), config, and data files (Parquet / JSONL / CSV). Use after the dataset is cleaned and packaged (ideally via the parquet-jsonl-package skill) and the user wants…
Restructure JSON or JSONL data — pivot flat records into nested hierarchy, un-nest deeply nested structures, group by keys, promote/demote fields in the hierarchy, split arrays into sibling objects. Use when JSON shape needs to change (e.g. flat rows →…
Package a dataset as Parquet and/or JSONL for storage, distribution, or upload to data platforms (Hugging Face, S3, Wasabi, etc.). Handles partitioning, compression, schema enforcement, and side-by-side emission of both formats. Use when the user wants to…
Standardise inconsistent country names in a dataset (e.g. "USA", "U.S.A.", "United States of America" → single canonical form). Use when a country column contains multiple spellings/aliases for the same country and the user wants them normalised.
Convert text-formatted numeric values (e.g. "$4.27", "1,234.56", "€1.2M", "3%", "(500)") into clean numeric columns, recording the original formatting (currency, scale, sign convention) in the data dictionary. Use when a column that should be numeric is typed…
Update an existing data dictionary to reflect new columns, changed types, renamed fields, dropped columns, or new transformations/provenance entries. Use after running any operation that modifies a dataset's schema (add-iso3166, enrich-with-currency,…