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Lancedb

Checked
poletron·vlatest·ai-ml·CC0-1.0

Lancedb efficiently converts Markdown files to LanceDB databases for structured data extraction and analysis, streamlining workflows.

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 odjaramillo/custom-rules --skill lancedb -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

poletron

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 poletron/custom-rules at aba6668777.

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

Commit
aba6668777
Manifest
registry/skills/lancedb/SKILL.md
Scanned
6 files, 290 lines
Copies in repo
2 — all of them scanned
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 lancedb skill provides access to LanceDB, a vector database. It allows AI agents to store and retrieve data efficiently using vector embeddings, enabling capabilities like semantic search and similarity comparisons. This facilitates more intelligent interactions and decision-making based on contextual understanding.

When to use it

  • Semantic Search: Build an agent that can find relevant documents or information based on the meaning of a query, rather than just keyword matches.
  • Recommendation Systems: Create agents that suggest products, content, or actions based on user preferences and past behavior stored as vectors.
  • Knowledge Base Retrieval: Allow an agent to quickly access and utilize a large knowledge base by storing information as vector embeddings for efficient retrieval.
  • Similarity Matching: Develop agents that identify similar items or concepts within a dataset, such as finding duplicate entries or grouping related data points.

Key capabilities

  • Vector database integration
  • Semantic search functionality
  • Similarity comparison
  • Data storage and retrieval using vector embeddings

Example prompts

  • "Find documents similar to 'customer service best practices'."
  • "Recommend products based on this user's purchase history."
  • "Retrieve the information about project X from the knowledge base."

Tips & gotchas

Requires an account with LanceDB and appropriate API keys for authentication. Familiarity with vector embeddings and database concepts is helpful to maximize effectiveness.