全部能力

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search,…

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices,…

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces.…

1688选品铺货 + 商机趋势专家。用于：(1) 在1688搜索商品/选品找货源 (2) 查询已绑定的下游店铺 (3) 将商品铺货到抖音/拼多多/小红书/淘宝等平台 (4) 配置1688 AK密钥 (5) 查看即时商机热榜 (6) 查看类目/行业趋势与价格分布 (7) 生成店铺经营日报并输出主营商品选品建议。 触发词：帮我找商品、在1688搜、选品、铺货、上架、查店铺、配置AK、商机、热榜、排行榜、趋势、价格分布、经营日报、店铺日报、动销分析、经营分析、选品建议、1688找货。

Loads coding rules from Qodo most relevant to the current coding task by generating a semantic search query from the assignment. Use when Qodo is configured…

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval…

Google Gemini File Search for managed RAG with 100+ file formats. Use for document Q&A, knowledge bases, or encountering immutability errors, quota issues,…

Expert in graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns. Specializes…

Query knowledge artifacts across all locations. Triggers: "find learnings", "search patterns", "query knowledge", "what do we know about", "where is the plan".

Look up current research information using parallel-cli search (primary, fast web search), the Parallel Chat API (deep research), or Perplexity…

Create a new sequentially numbered database migration with up/down SQL files

Validate pending migrations for foreign key consistency, rollback safety, and best practices

sqlite-vec extension for vector similarity search in SQLite. Use when storing embeddings, performing KNN queries, or building semantic search features.…

Smoke test for alicloud-ai-search-dashvector. Validate minimal authentication, API reachability, and one read-only query path.

DEFAULT search tool for ALL search/lookup needs. Multi-source search and deduplication layer with intent-aware scoring. Integrates Brave Search (web_search), Exa, Tavily, and Grok to provide high-coverage, high-quality results. Automatically classifies query intent and adjusts search strategy, scoring weights, and result synthesis. Use for ANY query that requires web search — factual lookups, research, news, comparisons, resource finding, "what is X", status checks, etc. Do NOT use raw web_search directly; always route through this skill.

Generate architecture documents using templates with diagram integration. Use for creating C4 diagrams, viewpoint documents, and technical overviews.

Materialize documentation for SQL syntax, data ingestion, concepts, and best practices. Use when users ask about Materialize queries, sources, sinks, views, or…

Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.

Create and edit Obsidian Bases (.base files): Obsidian's native database layer for dynamic tables, card views, list views, filters, formulas, and summaries…

Implement GraphRAG patterns combining knowledge graphs with retrieval for complex reasoning. Use this skill when building RAG over interconnected data or needing relationship-aware retrieval. Activate when: GraphRAG, knowledge graph, graph retrieval, entity relationships, Neo4j RAG, graph database, connected data.