Total 54,386 skills, Data Processing has 2785 skills
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Queries the UniBind database for experimentally validated transcription factor (TF) binding sites. Use when retrieving direct TF-DNA interaction datasets, downloading binding site coordinates (BED/FASTA) for local analysis, or listing available datasets by species, cell line, or TF name. Don't use to query specific intervals, locations, genes, motif models or expression data.
LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++.
cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
Backtrader 开源量化回测框架，支持多数据源、多策略、多周期回测与实盘交易，纯Python实现。当用户明确提及backtrader相关策略开发时使用。
Builds routing and origin-destination analysis workflows in CARTO. Triggers when the user mentions routing, route calculation, travel time, travel distance, OD matrix, origin-destination, isoline, isochrone, isodistance, catchment area, reachable area, drive time polygon, walk time polygon, service area, accessibility analysis, travel time matrix, distance matrix, commute patterns, trip flow, OD flow, mobility patterns, taxi trips, ride patterns, route geometry, shortest path, network distance, or wants to compute routes, generate isolines, build travel matrices, or analyze movement patterns between origins and destinations.
Datos de Google Finance via batchexecute (API RPC interna sin auth ni API key). Quote, OHLC intraday 1-min y 5-min, OHLC daily, financials masivos (income/balance/cashflow), earnings, analyst recommendations + opinions, descripcion empresa, peers, news, indices globales (Dow/S&P/NASDAQ/VIX/DAX), sectors heatmap. Cobertura mercados US (NASDAQ/NYSE) y argentinos (BCBA). ⚠️ API NO oficial — leer LIMITATIONS_TROUBLESHOOTING.md antes de uso productivo.
Market Data API de Alpaca: acciones, crypto, opciones. Historical y real-time data para 5000+ stocks.
Token unlock schedules, cliff events, daily emissions, allocation breakdowns. Use when checking upcoming unlocks, supply pressure, or vesting cliffs before a trade (e.g. ARB unlock, ENA emission, SUI cliff).
Corporate event opportunity scanner for A-share companies via Longbridge — identifies and analyses events that may create pricing dislocations: M&A / restructuring (asset injection / reverse merger), major shareholder increases / buybacks (positive signal), equity incentive plans (management alignment), index inclusion / exclusion (forced passive flows), and lockup expiry (potential selling pressure). Provides historical statistical patterns and trading window recommendations per event type. Triggers: "捕捉机会", "事件机会", "并购重组机会", "增持机会", "回购信号", "指数调整机会", "解禁压力", "事件套利", "捕捉機會", "事件機會", "並購重組機會", "增持機會", "回購信號", "指數調整機會", "解禁壓力", "event opportunity", "corporate event", "M&A opportunity", "buyback signal", "index inclusion", "lockup expiry", "event catalyst", "special situation", "event-driven".
Undetectable, adaptive, high-performance Python web data extraction. Automatically survives website structure changes, bypasses anti-bot systems (Cloudflare, WAFs), and outperforms BeautifulSoup/Scrapy. Includes stealth browser fetching, CSS/XPath selectors, CLI, interactive shell, and MCP AI server integration.
Set up end-to-end Change Data Capture (CDC) pipelines on Confluent Cloud using Debezium source connectors, Flink for transformation, and Tableflow for data lake integration. Supports JSON_SR, Avro, and Protobuf formats. Handles schemaless topics (plain JSON without SR) and multi-event topics. This skill handles the complete workflow from database to Iceberg/Delta tables. Use this skill when users want to capture database changes and materialize them into Iceberg or Delta Lake tables via Confluent Cloud Tableflow. Trigger phrases include "CDC to Tableflow", "database to Iceberg", "database to Delta Lake", "stream database changes to data lake", "set up Tableflow pipeline", "schemaless topic to Tableflow", or "multi-event topic to Iceberg". Do NOT trigger for general CDC, Debezium, or database replication requests that do not involve Tableflow or Iceberg/Delta Lake as the destination.
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit