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From pinecone
Lists all available Pinecone skills, activation triggers, and setup requirements including API key, MCP server, CLI, and uv.
How this skill is triggered — by the user, by Claude, or both
Slash command
/pinecone:helpThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. It's useful for building semantic search, retrieval augmented generation, recommendation systems, and agentic applications.
Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. It's useful for building semantic search, retrieval augmented generation, recommendation systems, and agentic applications.
Here's everything you need to get started and a summary of all available skills.
export PINECONE_API_KEY="your-key"
| Tool | What it enables | Install |
|---|---|---|
| Pinecone MCP server | Use Pinecone directly inside your AI agent/IDE without writing code | Setup guide |
Pinecone CLI (pc) | Manage all index types from the terminal, batch operations, backups, CI/CD | brew tap pinecone-io/tap && brew install pinecone-io/tap/pinecone |
| uv | Run the packaged Python scripts included in these skills | Install uv |
| Skill | What it does |
|---|---|
pinecone:quickstart | Step-by-step onboarding — create an index, upload data, and run your first search |
pinecone:query | Search integrated indexes using natural language text via the Pinecone MCP |
pinecone:cli | Use the Pinecone CLI (pc) for terminal-based index and vector management |
pinecone:assistant | Create, manage, and chat with Pinecone Assistants for document Q&A with citations |
pinecone:mcp | Reference for all Pinecone MCP server tools and their parameters |
pinecone:full-text-search | Build a full-text-search index — schema design, safe bulk ingestion, and query construction (text / query_string / dense / sparse scoring with text-match and metadata filters). Preview API (2026-01.alpha); requires pinecone Python SDK ≥ 9.0. |
pinecone:docs | Curated links to official Pinecone documentation, organized by topic |
pinecone:n8n | Build n8n workflows with the Pinecone Assistant node or Pinecone Vector Store node, including best practices and full workflow JSON generation |
Just getting started? → pinecone:quickstart
Want to search an index you already have?
pinecone:query (uses MCP)pinecone:cliWorking with documents and Q&A? → pinecone:assistant
Building a full-text search index (BM25-style keyword/phrase matching, optionally combined with dense or sparse vectors)? → pinecone:full-text-search (preview API, needs pinecone Python SDK ≥ 9.0)
Building an n8n workflow with Pinecone (RAG pipeline, chat with docs)? → pinecone:n8n
Need to manage indexes, bulk upload vectors, or automate workflows? → pinecone:cli
Looking up API parameters or SDK usage? → pinecone:docs
Need to understand what MCP tools are available? → pinecone:mcp
npx claudepluginhub pinecone-io/pinecone-claude-code-plugin --plugin pineconeReferences curated Pinecone documentation links on indexes, upsert, search, metadata filtering, APIs, and SDKs. Use when coding Pinecone integrations or looking up parameters.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
Designs and optimizes vector database architectures for semantic search, RAG, and recommendation systems using Pinecone, Weaviate, Qdrant, Milvus, and pgvector.