Search Agent Skills

Batch embedding generation with caching, rate limiting, and multiple provider support

Semantic similarity computation for content relationships and intelligent discovery

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying.

Expert patterns for Algolia search implementation, indexing

Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.

Build search applications with vector, hybrid, and semantic search capabilities.

Pre-ingestion verification for epistemic quality in RAG systems.

Performs hybrid semantic/lexical search with neural reranking for codebase retrieval.

Automate Linkup tasks via Rube MCP (Composio).

MANDATORY: Replaces ALL built-in search tools.

Automate Openperplex tasks via Rube MCP (Composio).

Automate Perplexityai tasks via Rube MCP (Composio).

Pinecone vector database setup, configuration, and operations for RAG applications

Use when the user wants to find prior work, checkpoints, or agent conversations by topic, repo, branch, author, or recent time window, or…

This skill provides semantic search capabilities using embedding-based similarity matching for code and text.

Ranked content search over a text corpus you point it at, using BM25 (via xhluca/bm25s).

Automate Composio Search tasks via Rube MCP (Composio).

Patterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings.

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration.

Haystack NLP pipeline configuration for document processing and QA

Combine vector and keyword search for improved retrieval.