Total 54,003 skills, Data Processing has 2767 skills
Showing 12 of 2767 skills
Deep dive on a Polymarket market — OHLCV, orderbook, top holders, positions, trades, and PnL leaderboard. Use when analysing a specific prediction market.
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
Use this skill when calculating, analyzing, or reporting SaaS business metrics. Triggers on MRR, ARR, churn rate, LTV, CAC, LTV:CAC ratio, cohort analysis, net revenue retention, expansion revenue, board deck metrics, investor reporting, unit economics, payback period, or SaaS financial modeling. Covers metric definitions, formulas, spreadsheet implementation, cohort tables, and board-ready reporting for founders, finance teams, and growth operators.
This skill should be used when users need to analyze CSV or Excel files, understand data patterns, generate statistical summaries, or create data visualizations. Trigger keywords include "analyze CSV", "analyze Excel", "data analysis", "CSV analysis", "Excel analysis", "data statistics", "generate charts", "data visualization", "分析CSV", "分析Excel", "数据分析", "CSV分析", "Excel分析", "数据统计", "生成图表", "数据可视化".
Design a FHIR R4 data model for a healthcare application by mapping clinical concepts to resources, terminology, and implementation-ready relationships.
Salesforce Data Cloud Act phase. TRIGGER when: user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. DO NOT TRIGGER when: the task is segment creation (use sf-datacloud-segment), data retrieval/search work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Patterns for Domo Custom Connector IDE work only (not Domo App Platform custom apps).
Help developers new to Elasticsearch get from zero to a working search experience. Guide them through understanding their intent, mapping their data, and building a search experience with best practices baked in. Use this when developers are new to Elasticsearch and need help getting started with their search use case.
Comprehensive SAC scripting skill for SAP Analytics Cloud Analytics Designer and Optimized Story Experience. This skill should be used when the user asks to "create SAC script", "debug Analytics Designer", "optimize SAC performance", "planning operations in SAC", "filter data in SAC", "use DataSource API", "chart scripting", "table manipulation", "SAC event handlers", "version management", "data locking", "Optimized Story Experience API", "OSE scripting", "OSE widget API", "OSE DataSource", "story scripting API", "OSE planning API", "OSE method", "optimized story", "SAC story scripting", "story script", "SAC scripting", or works with SAC widgets, planning models, or analytics applications.
Design enrichment columns that bridge research hypotheses to list enrichment. Two modes: segmentation (columns that score hypothesis fit per company) and personalization (columns for company-specific hooks). Interactive column design with the user. Outputs ready-to-run column_configs for list-enrichment. Triggers on: "data points", "enrichment columns", "column design", "what to research", "data point builder", "build columns", "segmentation columns", "personalization columns".
Parseur et explicateur complet du format HPK (format de message propriétaire santé). Supporte plus de 100 types de messages couvrant l'administration des patients (ID, MV, CV), la chaîne logistique (PR, FO, MA, CO, LI, RO, FA), les stocks (SO, IM), la structure organisationnelle (ST, UT) et les opérations financières (RD, DD). Utilise @erp-pas/hpk-dictionary comme source de vérité. Valide la structure, extrait les champs, explique le contexte métier, mappe vers HL7 v2.5/IHE PAM et aide au dépannage des problèmes d'intégration.