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Found 2,503 Skills
Generate TTS audio, upload to object storage, and return public audio URLs through MCP.
Refactor MoonBit code to be idiomatic: shrink public APIs, convert functions to methods, use pattern matching with views, add loop invariants, and ensure test coverage without regressions. Use when updating MoonBit packages or refactoring MoonBit APIs, modules, or tests.
Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.
How to read data from the Sui network. Use when choosing or implementing a data access strategy — queries for on-chain state, indexing pipelines, historical lookups, event subscriptions, cross-chain reads, or off-chain blob storage. Covers the three live Sui APIs (gRPC, GraphQL RPC, deprecated JSON-RPC), the Archival Store, the General-Purpose Indexer, the `sui-indexer-alt` custom indexing framework, and Walrus for off-chain blobs.
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
Build with Firestore NoSQL database - real-time sync, offline support, and scalable document storage. Use when: creating collections, querying documents, setting up security rules, handling real-time listeners, or troubleshooting permission-denied, quota exceeded, invalid query, or offline persistence errors. Prevents 10 documented errors.
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
Guides the agent through building LLM-powered applications with LangChain and stateful agent workflows with LangGraph. Triggered when the user asks to "create an AI agent", "build a LangChain chain", "create a LangGraph workflow", "implement tool calling", "build RAG pipeline", "create a multi-agent system", "define agent state", "add human-in-the-loop", "implement streaming", or mentions LangChain, LangGraph, chains, agents, tools, retrieval augmented generation, state graphs, or LLM orchestration.
Best practices for building trading bots, arbitrage detectors, and high-performance trading systems with MMT. Use when building automated trading strategies, cross-exchange arbitrage, real-time market analysis, or backtesting systems using MMT's multi-exchange API.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
Visualization toolkit for mapping partner landscape, coverage, and priorities.
Specialized in the `keyv-file` adapter for Keyv. Use this when the user needs persistent file-based storage with features like batch writing (writeDelay), TTL management (expiredCheckDelay), and the `makeField` API for direct access.