| 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
- Profile the dataset — column list, types, null rates, sample values.
- 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.
- 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.
- Rank by value × feasibility — high-value / high-feasibility items first. Let the user pick.
- 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.
- 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).
