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".From its SKILL.md
npx -y skills add pinecone-io/gemini-cli-extension --skill assistantAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
4 things to look at
- skips confirmationTells the agent to proceed without asking first, 1 time: "Proactively handle these patterns without requiring explicit commands".
- reads credentialsReads from 2 credential sources: `PINECONE_API_KEY` and 1 more.
- 23 stars23 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 2 commands, including `uv run scripts/script_name.py [arguments]` and 1 more.
SKILL.md
3.2 KB, 730 tokens by cl100k_base, as published. Nobody here has run it
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
Upload: "upload my docs", "add files to my assistant", "index my documentation" → See references/upload.md
Sync: "sync my docs", "update my assistant", "keep assistant in sync", "refresh from ./docs" → See references/sync.md
Chat: "ask my assistant about X", "what does my assistant know about X", "chat with X" → See references/chat.md
Context: "search my assistant for X", "find context about X" → See 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 → ask the user 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"
uv run scripts/create.py --name docs-botuv run scripts/upload.py --assistant docs-bot --source ./docsuv run scripts/chat.py --assistant docs-bot --message "what are the main features?"
Prerequisites
PINECONE_API_KEYmust be available — terminal:export PINECONE_API_KEY="your-key", or add to a.envfile and run scripts withuv run --env-file .env scripts/...uvmust be installed — install uv- Get a free API key at: https://app.pinecone.io/?sessionType=signup
What ships with it: 12 files
51.0 KB alongside SKILL.md, 6 of them executable