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Found 2,794 Skills
Turso (Limbo) database helper — an in-process SQLite-compatible database written in Rust. Formerly known as libSQL / libsql. Replaces @libsql/client, libsql-experimental for Turso use cases. Works in Node.js, browser (WASM + OPFS for persistent local storage), React Native, and server-side. Features: vector search, full-text search, CDC, MVCC, encryption, remote sync. SDKs: JavaScript (@tursodatabase/database), Browser/WASM (@tursodatabase/database-wasm), React Native (@tursodatabase/sync-react-native), Rust (turso), Python (pyturso), Go (tursogo). This skill contains all SDK documentation needed to use Turso — do NOT search the web for Turso/libsql docs.
Configure data accelerators for local materialization and caching in Spice (Arrow, DuckDB, SQLite, Cayenne, PostgreSQL, Turso). Use when asked to "accelerate data", "enable caching", "materialize dataset", "configure refresh", "set up local storage", "improve query performance", "choose an accelerator", or "configure snapshots".
Use this skill for pull request workflows - creating PRs (branch, commit, push, open), reviewing PRs (code quality, test coverage, issue fixing), or merging PRs (CI checks, merge, cleanup). Handles the complete PR lifecycle via gh CLI. Triggers included, "create PR", "open PR", "review PR", "merge PR".
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Query fan-out coverage for AI visibility. Covers semantic variation analysis and sub-question targeting.
Appwrite Python SDK skill. Use when building server-side Python applications with Appwrite, including Django, Flask, and FastAPI integrations. Covers user management, database/table CRUD, file storage, and functions via API keys.
Retrieve consensus price targets for any stock using Octagon MCP. Use when you need the average, median, high, and low analyst price targets to evaluate upside/downside potential and analyst agreement.
Markets orchestration — connects ESPN live schedules with Kalshi & Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms. Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value. Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.
A Yahoo Finance (yfinance) powered financial analysis tool. Get real-time quotes, generate high-resolution charts with moving averages + indicators (RSI/MACD/Bollinger/VWAP/ATR), summarize fundamentals, and run a one-shot report that outputs both a text summary and a Pro chart.
Write tests following TDD principles. Use when implementing features, fixing bugs, or adding test coverage. Covers e2e, integration, and unit testing patterns.
Audit rapidly generated or AI-produced code for structural flaws, fragility, and production risks.
Complete Google Gemini API reference for 2026. Use whenever writing code that calls Gemini models. Covers the google-genai SDK, Gemini 3/3.1 models, thought signatures, thinking config, Interactions API, File Search (managed RAG), Computer Use, URL Context, Nano Banana image gen, Live API, ephemeral tokens, TTS, Veo video gen, Lyria music gen, and all tools. ALWAYS prefer `from google import genai` over any legacy import. Use this skill for ANY Gemini API question, even simple ones.