Total 50,473 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Design dashboards, write analytical SQL, define KPIs, and manage stakeholder analytics requirements. Cover chart selection, data storytelling, cohort/funnel analysis, metric definitions, and BI tool patterns (Tableau, Looker, Power BI). Triggers on "build dashboard", "design dashboard", "write analytical SQL", "cohort analysis", "funnel analysis", "define KPI", "define metric", "reporting requirements", "data storytelling", "stakeholder analytics", "retention analysis", or "BI report". For business model canvas, TAM/SAM/SOM, and competitor monetization research, use business-model-researcher—not bi-analyst. For building warehouse marts, dbt models, tests, and lineage—not dashboards—use analytics-data-engineer.
Use when analyzing research datasets, cleaning tabular data, selecting statistical tests, producing result tables, creating publication figures, or moving notebook logic into reproducible code.
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing ADF pipelines, mapping data flows, SHIR/SSIS IR, SAP CDC, or CI/CD with ARM/DevOps, and other Azure Data Factory related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics), Azure Data Explorer (use azure-data-explorer).
Analyze unit economics for PE targets — ARR cohorts, LTV/CAC, net retention, payback periods, revenue quality, and margin waterfall. Essential for software/SaaS, recurring revenue, and subscription businesses. Use when evaluating revenue quality, building a cohort analysis, or assessing customer economics. Triggers on "unit economics", "cohort analysis", "ARR analysis", "LTV CAC", "net retention", "revenue quality", or "customer economics".
Placekey integration. Manage data, records, and automate workflows. Use when the user wants to interact with Placekey data.
Use Ibis for database-agnostic data access in Python. Use when writing data queries, connecting to databases (DuckDB, PostgreSQL, SQLite), or building portable data pipelines that should work across backends.
Build ETL pipelines and analytics dashboards using Harvard Art Museums API with SQL and Streamlit
Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.
Extracts first and/or last frames of every shot from a video using adaptive scene detection. Use this skill when the user says "extract frames", "get shot frames", "pull frames", "shot breakdown", "scene detect", "first frame of each shot", "last frame of each shot", "extract shots from video", or wants to extract key frames at shot cut points from a video file.
Builds territory planning workflows in CARTO combining territory balancing and location allocation. Triggers when the user mentions territory balancing, territory planning, sales territories, service zones, workload distribution, balanced territories, location allocation, facility placement, optimal locations, maximize coverage, minimize cost, minimize travel distance, depot placement, hub placement, warehouse siting, response time optimization, demand coverage, or wants to divide an area into balanced regions or find optimal facility locations.
Guides the user through spatial enrichment workflows — triggered by requests to enrich, add demographics, estimate population around locations, compute spatial features, sociodemographic analysis, "what's around" queries, buffer/isochrone + join patterns, or trade area enrichment.
Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.