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Llm_Evaluation

Checked
vuralserhat86·vlatest·ai-ml

Assess LLM outputs based on provided criteria like accuracy, relevance, and safety, generating detailed feedback reports.

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 vuralserhat86/antigravity-agentic-skills --skill llm_evaluation -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

vuralserhat86

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 vuralserhat86/antigravity-agentic-skills at f033922af6.

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

Commit
f033922af6
Manifest
skills/llm_evaluation/SKILL.md
Scanned
1 file, 477 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 allows an AI agent to evaluate the output of other language models (LLMs). It can assess responses based on criteria like helpfulness, accuracy, and relevance. The evaluation process provides a structured feedback mechanism for improving LLM performance in various applications.

When to use it

  • Automated Feedback Loops: Integrate into workflows where continuous improvement of an LLM is needed, such as chatbot training or content generation pipelines.
  • A/B Testing: Compare the quality of responses from different LLMs or prompt variations.
  • Quality Assurance: Regularly check the output of an LLM to ensure it meets predefined standards and identify potential issues.

Key capabilities

  • LLM Output Evaluation
  • Helpfulness Assessment
  • Accuracy Verification
  • Relevance Scoring

Example prompts

  • "Evaluate this response: [insert LLM response here] based on helpfulness, accuracy, and relevance."
  • "Score the following text for its adherence to a professional tone: [insert LLM generated text]."
  • "Compare these two responses and tell me which is better and why: [response 1], [response 2]"

Tips & gotchas

The effectiveness of this skill depends on clear evaluation criteria. Providing specific guidelines or examples will improve the quality of the assessment.