Total 50,523 skills, Data Processing has 2561 skills
Showing 12 of 2561 skills
🔍 Find API | 寻找可靠数据源 TRIGGERS: Use when agent needs to fetch external data, user mentions "reliable data source", "数据源", "API", or when web scraping is inefficient/inaccurate. A comprehensive guide to reliable data APIs across all domains. Helps agents find the best APIs instead of inefficient web scraping. Currently covers: Stock/Financial data, Weather, News, Maps, and more domains coming soon. 触发条件：Agent 需要获取外部数据、用户提到"可靠数据源"、"数据源"、"API"，或网页爬取效率低/不准确时。 跨领域可靠数据 API 的综合指南。 帮助 Agent 找到最佳 API，避免低效的网页爬取。 目前覆盖：股票/金融数据、天气、新闻、地图，更多领域持续完善中。
Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.
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.
Analyze stock correlations to find related companies and trading pairs. Use this skill whenever the user asks about correlated stocks, related companies, sector peers, trading pairs, or how two or more stocks move together. Triggers include: "what correlates with NVDA", "find stocks related to AMD", "correlation between AAPL and MSFT", "what moves with", "sector peers", "pair trading", "correlated stocks", "when NVDA drops what else drops", "find me a pair for", "stocks that move together", "beta to", "relative performance", "which stocks follow AMD", "supply chain partners", "correlation matrix", "co-movement", "related tickers", "sympathy plays", "if GOOGL moves what else moves", "semiconductor peers", "compare correlation", "hedging pair", "sector clustering", "realized correlation", "rolling correlation", or any request about finding stocks that move in tandem or inversely. Also triggers when the user mentions well-known pairs like AMD/NVDA, GOOGL/AVGO, LITE/COHR and wants to understand or find similar relationships. Always use this skill even if the user only provides one ticker — infer that they want to find correlated peers.
Analyze commodity markets including futures curve dynamics, roll yield, and supply/demand fundamentals. Use when the user asks about commodity investing, commodity ETFs, contango, backwardation, roll yield, commodity indices (GSCI, BCOM), or commodities as an inflation hedge. Also trigger when users mention 'oil prices', 'gold as a safe haven', 'agricultural futures', 'convenience yield', 'storage costs', 'natural gas', 'copper demand', or ask why commodity ETF returns differ from spot price changes.
Query the ExoPriors Scry API -- SQL-over-HTTPS search across 229M+ entities spanning forums, papers, social media, government records, and prediction markets. Includes cross-platform author identity resolution (actors, people, aliases), OpenAlex academic graph navigation (authors, citations, institutions, concepts), shareable artifacts, and structured agent judgements. Use when the task involves: Scry API, ExoPriors, /v1/scry/query, scry.search, scry.entities, materialized views, corpus search, epistemic infrastructure, 229M entities, lexical search, BM25, structured agent judgements, scry shares, cross-corpus analysis, who is this person, cross-platform identity, OpenAlex, citation graph, coauthor graph, academic papers, author lookup. NOT for: semantic/vector search composition or embedding algebra (use scry-vectors), LLM-based reranking (use scry-rerank), or the user's own local Postgres / non-ExoPriors data sources.
This skill should be used when the user asks to "use NumPy", "write NumPy code", "optimize NumPy arrays", "vectorize with NumPy", or needs guidance on NumPy best practices, array operations, broadcasting, memory management, or scientific computing with Python.
This skill should be used when the user asks to "validate a DataFrame with pandera", "write a pandera schema", "use pandera DataFrameModel", "add data validation to a pipeline", or needs guidance on pandera best practices for data quality.
OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-integration-procedure), authoring OmniScripts (use sf-omniscript), or analyzing cross-component dependencies (use sf-omnistudio-analyze).
Query the public PANews API for the currently bundled read-only workflows, article search, listings, rankings, and daily must-reads.
What is the state of the Hyperliquid perp market? Top contracts by volume/OI, trader leaderboard, and SM perp activity.
How has a wallet's portfolio changed over time? Historical balances, current snapshot, and per-token PnL.