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Pinecone

Checked
davila7·vlatest·ai-ml·MIT

Pinecone enables efficient semantic search by indexing and querying vector embeddings, quickly finding similar content for applications like recommendation & knowledge retrieval.

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 davila7/claude-code-templates --skill pinecone -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

davila7

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/rag-pinecone/SKILL.md
Scanned
2 files, 503 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 skill enables AI agents to interact with vector databases, allowing efficient storage and retrieval of high-dimensional data. It supports operations like adding vectors, querying nearest neighbors, and managing indexes.

When to use it

  • To build recommendation systems that rely on similarity searches between items.
  • For natural language processing tasks where semantic search is required.
  • When developing AI applications that need fast access to large datasets using vector embeddings.

Key capabilities

  • Integration with Pinecone's API for managing vectors and indexes
  • Support for efficient nearest neighbor queries
  • Scalable storage of high-dimensional data

Example prompts

  • "Find the three most similar documents to this query using the Pinecone database."
  • "Add these new text embeddings to the Pinecone index for later retrieval."
  • "Query the Pinecone vector store for items closest to the given embedding."

Tips & gotchas

  • Ensure you have a valid Pinecone API key and project set up before using this skill.
  • Be mindful of data size limits and cost implications when working with large datasets.