Total 50,502 skills, Data Processing has 2560 skills
Showing 12 of 2560 skills
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".
Comprehensive Stata reference for writing correct .do files, data management, econometrics, causal inference, graphics, Mata programming, and 20 community packages (reghdfe, estout, did, rdrobust, etc.). Covers syntax, options, gotchas, and idiomatic patterns. Use this skill whenever the user asks you to write, debug, or explain Stata code.
Develop high-performance C/C++ plugins for Stata using the stplugin.h SDK. Use when the user asks to create a Stata plugin, write C/C++ code for Stata, accelerate a Stata command with C, build cross-platform Stata plugins, or translate/port a Python or R package into Stata. Covers the full lifecycle: SDK setup, data flow, memory safety, .ado wrappers with preserve/merge, cross-platform compilation, performance optimization (pthreads, pre-sorted indices, XorShift RNG), debugging, and distribution via net install. Also includes a translation workflow for porting Python/R packages to Stata — wrapping existing C++ backends when available, or writing C from scratch when not.
Expert knowledge for Azure Data Manager for Agriculture development including limits & quotas, security, configuration, and integrations & coding patterns. Use when setting up BYOL creds/Private Link, ag data ingestion/IoT, AI/nutrient APIs, throttling, or Event Grid logs, and other Azure Data Manager for Agriculture related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks).
Adds Excel Online (Business) connector to a Power Apps code app. Use when reading or writing Excel workbook data from OneDrive or SharePoint.
Add research-powered enrichment columns to Extruct company tables. Use when the user wants to add enrichment columns (e.g. funding, verticals, tech stack) to an existing Extruct table, run column configs from enrichment-design, or monitor enrichment progress. Triggers on: "enrich", "add column", "add data point", "research column", "enrich table", "enrichment", "add a field", "run enrichment", "monitor enrichment".
Extract speaker names, titles, companies, and bios from conference websites. Supports direct HTML scraping and Apify web scraper fallback for JS-heavy sites. Use for pre-event research and outreach targeting.
Search PubMed for meta-analyses on a given medical topic using NCBI E-utilities API
Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.
Guide for querying DeFi yield and APY data using get_yield_pools. Covers pool filtering by token, chain, protocol, category, stablecoin-only mode, and capacity assessment. Explains APY conventions, lending vs borrowing rates, and sort options. Use when users ask about yields, APY, lending rates, borrowing costs, best pools, or DeFi yield strategies.
Data management skill. It provides capabilities of querying, creating, updating and deleting form data. It is triggered when users need to "query data", "create data", "update data" or "delete data".
Use this skill when the user wants to search the DataHub catalog, discover entities, answer ad-hoc questions about their data, find datasets, or browse by platform or domain. Triggers on: "search DataHub", "find datasets", "who owns X", "what tables contain PII", "what columns does X have", or any request to search, discover, browse, or answer one-off questions about DataHub metadata. For lineage questions ("what feeds into X"), use `/datahub-lineage`. For systematic audits ("how complete is our metadata"), use `/datahub-audit`.