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Doc To Vector Dataset Generator

Checked
patricio0312rev·vlatest·ai-ml·MIT

This skill automatically converts documents into vector datasets, streamlining data preparation for semantic search and AI 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 patricio0312rev/skills --skill doc-to-vector-dataset-generator -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

patricio0312rev

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 patricio0312rev/skills at 79ea6af485.

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

Commit
79ea6af485
Manifest
ai-engineering/doc-to-vector-dataset-generator/SKILL.md
Scanned
1 file, 224 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 converts raw documents into vector datasets, enabling AI agents to ingest unstructured text for semantic search and retrieval tasks. It bridges the gap between static file formats and machine-readable embeddings suitable for Large Language Model contexts.

When to use it

  • You need to prepare internal wikis or PDF manuals for an RAG (Retrieval-Augmented Generation) system.
  • You want to transform large batches of text files into a format compatible with vector databases like Pinecone or Chroma.
  • Your AI agent requires semantic understanding of document content rather than simple keyword matching.
  • You are building a knowledge base where users must query natural language against stored documentation.

Key capabilities

  • Ingests various document formats and processes them into structured data.
  • Generates embeddings to create vector representations of text content.
  • Outputs datasets optimized for downstream AI agent retrieval pipelines.

Example prompts

  • "Convert my uploaded PDF manual into a vector dataset for semantic search."
  • "Transform this batch of markdown files into a format ready for embedding storage."
  • "Generate a vector dataset from the provided text documents to power my knowledge base agent."

Tips & gotchas

Ensure your input documents are clean and free of excessive noise, as raw formatting can affect embedding quality. Verify that your downstream vector database supports the specific output schema generated by this tool.