Pinecone Agent Skills
Nine Pinecone skills: the agent creates a vector index, loads data, searches it and builds an assistant for documents
- 15 ★ on the repository
- Last commit
- MIT
Extended access
Why this level
- A Pinecone API key lets the agent create and delete indexes and load data into the account, where each index uses paid resources
- Your documents and texts go into the index and the Pinecone service stores them on its side
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/pinecone-agent-skillsDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- A Pinecone API key lets the agent create and delete indexes and load data into the account, where each index uses paid resources
- Your documents and texts go into the index and the Pinecone service stores them on its side
This is third-party code. Review the repository files before installing.
What it does
A library of nine skills from Pinecone, a vector database for semantic search and RAG. The `pinecone-quickstart` skill walks a newcomer down two paths: create an integrated index, load 9 sample sentences and run a first search, or create a Pinecone Assistant for questions over documents. `pinecone-query` searches indexes with plain text through the Pinecone MCP, and `pinecone-mcp` is a reference for all tools of that server. `pinecone-cli` runs index and vector work through the `pc` terminal utility. `pinecone-assistant` creates assistants and chats with them with citations. `pinecone-full-text-search` helps design an index schema with full-text search, load data safely and build queries scored by text, dense and sparse vectors with filters. `pinecone-n8n` builds n8n workflows with Pinecone nodes, `pinecone-docs` gives documentation links and `pinecone-help` explains what is needed to start. The skills follow the Agent Skills standard and work in Cursor, GitHub Copilot, Windsurf, Gemini CLI and other agents. For Claude Code users Pinecone recommends its own plugin.
Who it is for. Developers building semantic search, RAG or recommendations on Pinecone who want Cursor, Copilot or Gemini CLI to handle index setup and data loading by official rules.
Good fit when
- A first look at Pinecone: the agent creates an index, loads test sentences and shows a first semantic search
- Company documents must answer questions with citations: the `pinecone-assistant` skill creates an assistant and uploads files
- A product catalog needs a full-text index, and the schema with safe bulk loading is better delegated to the skill
- Index work runs from the terminal with the `pc` utility and the agent has to know its commands for all index types
Not a fit when
- The vector database is a different one: the skills know Pinecone only
- You work in Claude Code and want the official route: Pinecone recommends a separate plugin for it
- There is no Pinecone API key: without `PINECONE_API_KEY` the skills create nothing
Example request
Run the Pinecone quickstart: create an index, load the test sentences and show the search result for a productivity queryLimitations
- A Pinecone account and an API key in `PINECONE_API_KEY` are required, and sign-up is free.
- The `pinecone-query` skill and agent index operations need the Pinecone MCP, and `pinecone-cli` needs the `pc` utility installed by `brew install pinecone-io/tap/pinecone`.
- Bundled scripts run through `uv`.
- The library targets agents with Agent Skills support, and for Claude Code Pinecone advises its own plugin.
How to disable. Delete the `pinecone-*` skill folders from the agent skills directory. The API key is revoked in the Pinecone console, where created indexes are deleted too.
FAQ
How do I install the Pinecone skills?
Run `npx skills add pinecone-io/skills`. For Claude Code Pinecone recommends a separate official plugin from the `pinecone-claude-code-plugin` repository.
Where should a newcomer start?
With the `pinecone-quickstart` skill. It checks the key with a `list-indexes` call through the Pinecone MCP and asks which path to take: Database or Assistant. The `pinecone-help` skill briefly explains which skills and tools are needed.
What do the skills need to work?
A Pinecone account and an API key in `PINECONE_API_KEY`. Optionally: the Pinecone MCP for `pinecone-query`, the `pc` utility (`brew install pinecone-io/tap/pinecone`) for `pinecone-cli` and `uv` for bundled Python scripts.
How do these skills differ from the Pinecone MCP?
MCP gives the agent tools for calls, while the skills explain how to use them: step order, index schemas, queries and common mistakes. The `pinecone-query` and `pinecone-mcp` skills rely on the MCP server, and the others also work without it.
Do the skills work with n8n?
Yes, `pinecone-n8n` covers it. The skill builds workflows with the Pinecone Assistant or Pinecone Vector Store node, gives best practices and generates the full workflow JSON.
Discussion
No questions or reviews yet.
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Comparison with similar tools
Cards that share the main search query, and how they differ.
| Tool | Official | Authentication | Repository stars | Main difference |
|---|---|---|---|---|
| Pinecone Agent Skills (this card) | yes | 15 | ||
| Pinecone Developer MCP Server | yes | key is optional | 72 | the Pinecone MCP server with call tools rather than skills with step-by-step workflows |
| Weaviate Agent Skills | yes | 104 | skills for a different vector database, Weaviate | |
| Qdrant Skills | no | 267 | skills for a different vector database, Qdrant |
README in short
The page presents the library as official and shows a one-command install. A table describes the nine skills, the requirements section lists the account, key, MCP, `pc` utility and `uv`, and the end gives validation commands for contributors to the repository.
README badge
Original SKILL.md
--- name: pinecone-quickstart description: Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided tour of Pinecone's tools. --- # Pinecone Quickstart Welcome! This skill walks you through your first Pinecone experience using the tools available to you. In this quickstart, you will learn how to do a simple form of semantic search over some example data. <<clarify_style>> ## Prerequisites Before starting either path, verify the API key works by calling `list-indexes` via the Pinecone MCP. If it succeeds, proceed. If it fails, ask the user to set their key: <<api_key_setup>> Then retry `list-indexes` to confirm. ## Step 0: Choose Your Path Ask the user which path they want: - **Database** – Build a vector search index. Best for developers who want to store and search embeddings. Uses the Pinecone MCP + a Python upsert script. - **Assistant** – Build a document Q&A assistant. Best for users who want to upload files and ask questions with cited answers. No code required. --- ## Path A: Database Quickstart For each step, explain to the user what will happen. An overview is here: 1. Check if MCP is set 2. Create an integrated index with MCP 3. Upsert sample data using the bundled script (9 sentences across productivity, health, and nature themes) 4. Run a semantic search query and explore further queries 5. Optionally try reranking 6. Offer the complete standalone script ### Step 1 – Verify MCP is Available The prerequisite check already called `list-indexes`. If it succeeded, the MCP is working — proceed to Step 2. If it failed because MCP tools were unavailable (not an auth error):