pinecone:assistant
Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pinecone".
git clone --depth 1 https://github.com/pinecone-io/pinecone-claude-code-plugin /tmp/pinecone-assistant && cp -r /tmp/pinecone-assistant/skills/assistant ~/.claude/skills/pinecone-assistantSKILL.md
# Pinecone Assistant Pinecone Assistant is a fully managed RAG service. Upload documents, ask questions, get cited answers. No embedding pipelines or infrastructure required. > All scripts are in `scripts/` relative to this skill directory. > Run with: `uv run scripts/script_name.py [arguments]` ## Operations | What to do | Script | Key args | |---|---|---| | Create an assistant | `scripts/create.py` | `--name` `--instructions` `--region` | | Upload files | `scripts/upload.py` | `--assistant` `--source` `--patterns` | | Sync files (incremental) | `scripts/sync.py` | `--assistant` `--source` `--delete-missing` `--dry-run` | | Chat / ask a question | `scripts/chat.py` | `--assistant` `--message` | | Get context snippets | `scripts/context.py` | `--assistant` `--query` `--top-k` | | List assistants | `scripts/list.py` | `--files` `--json` | For full workflow details on any operation, read the relevant file in `references/`. --- ## Natural Language Recognition Proactively handle these patterns without requiring explicit commands: **Create:** "create an assistant", "make an assistant called X", "set up an assistant for my docs" → See [references/create.md](references/create.md) **Upload:** "upload my docs", "add files to my assistant", "index my documentation" → See [references/upload.md](references/upload.md) **Sync:** "sync my docs", "update my assistant", "keep assistant in sync", "refresh from ./docs" → See [references/sync.md](references/sync.md) **Chat:** "ask my assistant about X", "what does my assistant know about X", "chat with X" → See [references/chat.md](references/chat.md) **Context:** "search my assistant for X", "find context about X" → See [references/context.md](references/context.md) **List:** "show my assistants", "what assistants do I have" → Run `uv run scripts/list.py` --- ## Conversation Memory Track the last assistant used within the conversation: - When a user creates or first uses an assistant, remember its name - If user says "my assistant", "it", or "the assistant" → use the last one - Briefly confirm which assistant you're using: "Asking docs-bot..." - If ambiguous and multiple exist → use AskUserQuestion to clarify --- ## Multi-Step Requests Handle chained requests naturally. Example: > "Create an assistant called docs-bot, upload my ./docs folder, and ask what the main features are" 1. `uv run scripts/create.py --name docs-bot` 2. `uv run scripts/upload.py --assistant docs-bot --source ./docs` 3. `uv run scripts/chat.py --assistant docs-bot --message "what are the main features?"` --- ## Prerequisites - `PINECONE_API_KEY` must be available — `export PINECONE_API_KEY="your-key"` (or use a `.env` file with `uv run --env-file .env`) - `uv` must be installed — [install uv](https://docs.astral.sh/uv/getting-started/installation/) - Get a free API key at: https://app.pinecone.io/?sessionType=signup
Opens a link to join the Pinecone Discord, allowing users to learn from each other, contact the Pinecone team, and get help in our dedicated help channel.
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.
Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.
Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API (2026-01.alpha, public preview). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + readiness polling); query construction is documented inline in this skill — write `documents.search(...)` calls directly, validated against `pc.preview.indexes.describe(...)` output.
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.