vector-databasesDeep vector database workflow—embedding choice, index algorithms, recall/latency trade-offs, hybrid search, filtering, operational tuning, and cost. Use when...
Install via ClawdBot CLI:
clawdbot install clawkk/vector-databasesGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Usage Guide
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Scored May 18, 2026
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Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode.
Access AI-powered football match predictions from hergunmac.com. Use when the user asks about football/soccer match predictions, betting tips, match analysis, team statistics, head-to-head data, or upcoming match insights. Covers worldwide leagues with confidence scores, AI reasoning, and historical performance tracking.
Use when designing database schemas, writing migrations, optimizing SQL queries, fixing N+1 problems, creating indexes, setting up PostgreSQL, configuring EF Core, implementing caching, partitioning tables, or any database performance question.
browse MongoDB Atlas Admin API specifications and execute operations (if credentials provided).
Voyage AI embedding and reranking CLI integrated with MongoDB Atlas Vector Search. Use for: generating text embeddings, reranking search results, storing embeddings in Atlas, performing vector similarity search, creating vector search indexes, listing available models, comparing text similarity, bulk ingestion, interactive demos, and learning about AI concepts. Triggers: embed text, generate embeddings, vector search, rerank documents, voyage ai, semantic search, similarity search, store embeddings, atlas vector search, embedding models, cosine similarity, bulk ingest, explain embeddings.