Total 52,846 skills, Data Processing has 2644 skills
Showing 12 of 2644 skills
Tests join primitive (INNER JOIN)
Trade decision audit trail and statistics
Use when asked to normalize audio volume, match loudness, or apply peak/RMS normalization to audio files.
Enrich a CSV with any data field using a waterfall pattern: try multiple providers in sequence, stop at the first successful match. Prevents paying for duplicate lookups and maximizes fill rates. Triggers: - "enrich my lead list" - "add [field] to my CSV" - "waterfall enrichment" - "try multiple providers to find [data]" Requires: Deepline CLI — https://code.deepline.com
Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
Develop and deploy Lakeflow Jobs on Databricks. Use when creating data engineering jobs with notebooks, Python wheels, or SQL tasks. Invoke BEFORE starting implementation.
Track macroeconomic events, Fed rates, CPI, and economic indicators affecting crypto.
Review fixed income portfolios by pricing multiple bonds, retrieving reference data, analyzing cashflows, and running scenario analysis. Use when reviewing bond portfolios, computing portfolio duration and DV01, analyzing cashflow waterfalls, stress testing rate scenarios, or assessing portfolio composition.
Create advanced Excel pivot tables with calculated fields and slicers. Use when building data summaries or creating interactive dashboards. Trigger with phrases like 'excel pivot', 'create pivot table', 'data summary'.
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management
Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
Goldsky Turbo pipeline YAML reference — the authoritative source for field names, required vs optional fields, and valid values. Use whenever the user asks about specific YAML fields: what does `start_at: earliest` vs `latest` do, what fields does a postgres/clickhouse/kafka sink require, what is the `from:` field in a sink, how does `checkpoint` work, what's the syntax for `batch_size` or `primary_key`. Also use for validation errors like 'unknown field' or 'missing required field'. For interactive pipeline building end-to-end, use /turbo-builder instead.