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Vectordb

Checked
lobbi-docs·vlatest·ai-ml·MIT

Vectordb allows you to efficiently store and search vector embeddings for semantic similarity searches, boosting your AI applications' understanding.

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 thelobbi/claude --skill vectordb -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

lobbi-docs

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 lobbi-docs/claude at 1f04f9b061.

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

Commit
1f04f9b061
Manifest
.claude/skills/vector-db/SKILL.md
Scanned
1 file, 294 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

This skill provides access to a vector database. It allows you to store and retrieve data based on semantic similarity, enabling AI agents to perform tasks like finding similar documents or identifying related concepts. The vectordb skill facilitates efficient searching and retrieval of information within the vector space.

When to use it

  • Semantic Search: When needing to find documents or pieces of information that are conceptually similar but don't share keywords.
  • Recommendation Systems: To identify items (products, articles, etc.) that users might like based on their past behavior or preferences represented as vectors.
  • Knowledge Base Retrieval: To quickly retrieve relevant information from a large knowledge base by comparing the semantic meaning of a query to stored vector representations.
  • Anomaly Detection: Identify unusual data points that deviate significantly from established patterns within the vector space.

Key capabilities

  • Vector storage
  • Similarity search
  • Data retrieval based on semantic similarity

Example prompts

  • "Find documents similar to 'customer service best practices'."
  • "Recommend articles related to machine learning for beginners."
  • "Retrieve information about the concept of 'quantum entanglement'."

Tips & gotchas

The effectiveness of this skill depends heavily on the quality and relevance of the data stored within the vector database. Ensure your embeddings accurately represent the meaning you want to capture.