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Found 1,244 Skills
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Design or restyle DatoCMS plugins so they look and feel native to the DatoCMS UI. Use when users ask to make a plugin match the DatoCMS dashboard, polish plugin config screens, pages, sidebars, panels, modals, forms, tables, empty states, or overall plugin layout structure. This skill owns DatoCMS plugin design-system work, native-look restyling, and UI density or spacing cleanup. Prefer `datocms-react-ui` when a public component exists, and otherwise use raw React and CSS that reproduce DatoCMS spacing, typography, density, color, and interaction patterns without importing private CMS classes.
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
Build resilient data ingestion pipelines from APIs. Use when creating scripts that fetch paginated data from external APIs (Twitter, exchanges, any REST API) and need to track progress, avoid duplicates, handle rate limits, and support both incremental updates and historical backfills. Triggers: 'ingest data from API', 'pull tweets', 'fetch historical data', 'sync from X', 'build a data pipeline', 'fetch without re-downloading', 'resume the download', 'backfill older data'. NOT for: simple one-shot API calls, websocket/streaming connections, file downloads, or APIs without pagination.
Pragmatic patterns for building multiplayer games: matchmaking, tick loops, realtime state, interest management, and validation.
Documentation reference for using Browser Use Cloud — the hosted API and SDK for browser automation. Use this skill whenever the user needs help with the Cloud REST API (v2 or v3), browser-use-sdk (Python or TypeScript), X-Browser-Use-API-Key authentication, cloud sessions, browser profiles, profile sync, CDP WebSocket connections, stealth browsers, residential proxies, CAPTCHA handling, webhooks, workspaces, skills marketplace, liveUrl streaming, pricing, or integration patterns (chat UI, subagent, adding browser tools to existing agents). Also trigger for questions about n8n/Make/Zapier integration, Playwright/ Puppeteer/Selenium on cloud infrastructure, or 1Password vault integration. Do NOT use this for the open-source Python library (Agent, Browser, Tools config) — use the open-source skill instead.
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Generates multiple distinct design variants of a component or page, each with a completely different visual direction, then implements the chosen one. Use when the user asks to redesign, restyle, explore design options, create multiple visual directions, or compare design approaches for any UI element -- components, pages, sections, dashboards, landing pages, or full layouts.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
This skill should be used when the user asks for 'price of BTC', 'ETH ticker', 'show me the orderbook', 'market depth', 'BTC candles', 'OHLCV chart data', 'funding rate', 'open interest', 'mark price', 'index price', 'recent trades', 'price limit', 'list instruments', 'what instruments are available', or any request to query public market data on OKX CEX. All commands are read-only and do NOT require API credentials. Do NOT use for account balance/positions (use okx-cex-portfolio), placing/cancelling orders (use okx-cex-trade), or grid/DCA bots (use okx-cex-bot).
Use when building Django web applications or REST APIs with Django REST Framework. Invoke for Django models, ORM optimization, DRF serializers, viewsets, authentication with JWT.