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Embedding Ft

Checked
tiangong-lca·vlatest·ai-ml

This skill generates high-quality embeddings for text using Facebook's (FT) transformer model, boosting semantic understanding and similarity search.

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 tiangong-lca/agent-skills --skill embedding-ft -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

tiangong-lca

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 tiangong-lca/skills at 8cea578a18.

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

Commit
8cea578a18
Manifest
embedding-ft/SKILL.md
Scanned
7 files, 218 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, embedding-ft, allows AI agents to fine-tune text embeddings. It enables customization of embedding models to improve performance on specific tasks or datasets. This results in more accurate semantic representations for downstream applications like search and similarity matching.

When to use it

  • Improving Search Relevance: Fine-tune embeddings on a dataset of your company's product descriptions to enhance the relevance of internal search results.
  • Custom Similarity Matching: Train embeddings on a specialized corpus of legal documents to improve the accuracy of identifying similar cases.
  • Personalized Recommendations: Adjust embeddings based on user preferences and behavior for more tailored recommendations.
  • Domain-Specific Understanding: Adapt embeddings to better understand terminology and nuances within a particular industry or field.

Key capabilities

  • Fine-tuning text embedding models
  • Customization of embedding representations
  • Improved semantic understanding

Example prompts

  • "Fine-tune the embedding model using this dataset of customer reviews."
  • "Create embeddings for these product descriptions and optimize them for search relevance."
  • "Adjust the embedding model to better understand medical terminology."

Tips & gotchas

Requires a suitable training dataset specific to the desired application. The quality and size of the dataset significantly impact the effectiveness of fine-tuning.