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Pinecone Assistant

Listed
pinecone-io·vlatest·ai-ml·MIT

Pinecone Assistant leverages vector databases for intelligent retrieval and context understanding, boosting AI performance across various applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pinecone-io/skills --skill pinecone-assistant -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

pinecone-io

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: app.pinecone.io.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    app.pinecone.ioundeclared

    What it found (3 of 6)

    • skills/pinecone-assistant/scripts/chat.py:46contacts app.pinecone.io
      console.print("\nGet your API key from: https://app.pinecone.io/?sessionType=signup")
    • skills/pinecone-assistant/scripts/context.py:51contacts app.pinecone.io
      console.print("\nGet your API key from: https://app.pinecone.io/?sessionType=signup")
    • skills/pinecone-assistant/scripts/create.py:67contacts app.pinecone.io
      console.print("\nGet your API key from: https://app.pinecone.io/?sessionType=signup")

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned pinecone-io/skills at 885dc539fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
885dc539fb
Manifest
skills/pinecone-assistant/SKILL.md
Scanned
13 files, 1,528 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The pinecone-assistant skill enables AI agents to interact with Pinecone, a vector database. It allows agents to perform operations like indexing data, querying embeddings, and retrieving relevant information based on semantic similarity. This facilitates building applications that require efficient similarity search and retrieval of large datasets.

When to use it

  • Semantic Search: Build an agent capable of answering questions by searching through a knowledge base stored in Pinecone.
  • Recommendation Systems: Develop agents that recommend products or content based on user preferences represented as embeddings in Pinecone.
  • Image/Video Retrieval: Create applications where users can search for similar images or videos using embedding-based similarity search within Pinecone.
  • Chatbots with Memory: Enhance chatbots by storing conversation history and relevant context in Pinecone to improve response accuracy and personalization.

Key capabilities

  • Indexing data into a Pinecone index.
  • Querying a Pinecone index for similar vectors.
  • Retrieving information from a Pinecone index based on semantic similarity.

Example prompts

  • "Find the top 5 most similar documents to this query: [query text]"
  • "Index these data points into my 'product_catalog' Pinecone index: [data]"
  • "Retrieve all entries in the 'customer_support_logs' index with a similarity score greater than 0.8."

Tips & gotchas

  • Requires an active Pinecone account and API key to function correctly.
  • The quality of results depends heavily on the embeddings used for indexing data; ensure they accurately represent the semantic meaning.