data-enrichment

name	data-enrichment
description	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 making a dataset more analytically valuable but isn't sure what enrichments are feasible.

Data Enrichment

Brainstorm and evaluate enrichment options for a dataset. Produces a menu of candidate enrichments with feasibility, cost, and value notes.

When to invoke

• User asks "how could this dataset be enriched?" or "what could we add to make this more useful?".
• Dataset is already clean and the user is looking for next steps before analysis/modelling.

Procedure

1. Profile the dataset — column list, types, null rates, sample values.
2. Scan for enrichment-ready columns:
   • Geographic: country / city / region / address / coordinates → ISO codes, currency, language, population, timezone, continent, GDP, coordinates (geocoding).
   • Temporal: date / datetime → year, quarter, month, day-of-week, week-of-year, is-weekend, is-holiday (country-specific), season, fiscal-year.
   • Identifiers: VAT / company number / stock ticker / ISBN / DOI / wiki-ID → metadata lookups via public APIs (Wikidata, OpenCorporates, yfinance, crossref).
   • Monetary: amount + currency + date → FX-normalised value, inflation-adjusted value, common-base conversion.
   • Text: free text → language detection, sentiment, entities, embeddings, topic labels.
   • Email / domain: → domain, MX records, company lookup (handle PII carefully — see pii-flag).
   • IP address: → geolocation, ASN, ISP.
3. Emit an enrichment menu — for each candidate enrichment, record:
   • What it adds (columns and types).
   • Source (in-plugin skill, public dataset, public API, paid API, LLM).
   • Feasibility (high / medium / low — notes on rate limits, cost, accuracy).
   • Privacy / licensing considerations — especially for third-party API outputs.
   • Example output — one row of synthetic enrichment on a sample input value.
4. Rank by value × feasibility — high-value / high-feasibility items first. Let the user pick.
5. Link to executing skills — some enrichments map to plugin skills (add-iso3166, enrich-with-currency); others need a bespoke one-off script which this skill can scaffold but not run without user confirmation.
6. Write the menu to enrichment_options.md alongside the dataset.

Dependencies

pip install pandas

Plus optional per-enrichment libraries (flagged in the menu):

• pycountry, babel — geographic + currency.
• holidays — calendar-based enrichment.
• geopy — geocoding (respect usage policies).
• wikidata / SPARQLWrapper — entity enrichment.
• yfinance — market data.

Edge cases

• PII-heavy columns — if the field looks like PII (see pii-flag), note the risk in the menu and recommend reviewing legality before enriching via third-party services.
• Rate-limited APIs — estimate request count and warn if it exceeds free-tier limits.
• Low-accuracy enrichments (fuzzy entity matching, sentiment) — mark accuracy explicitly; do not oversell.
• Licence-restricted sources — flag anything that would contaminate the dataset's own licence (e.g. adding Google Places data to a CC-BY dataset).