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Evaluating Llms

Listed
ancoleman·vlatest·ai-ml·MIT

This skill assesses Large Language Model outputs for accuracy, coherence, and bias, ensuring higher-quality responses and informed decision-making.

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 ancoleman/ai-design-components --skill evaluating-llms -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

ancoleman

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: smith.langchain.com.

    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.

    Hosts contacted

    smith.langchain.comundeclared

    What it found

    • skills/evaluating-llms/examples/typescript/langsmith-integration.ts:293contacts smith.langchain.com
      console.log("View comparison at: https://smith.langchain.com/");
    • skills/evaluating-llms/examples/typescript/langsmith-integration.ts:383contacts smith.langchain.com
      console.error("Get your key at: https://smith.langchain.com/");
    • skills/evaluating-llms/examples/typescript/langsmith-integration.ts:407contacts smith.langchain.com
      console.log("1. View results at: https://smith.langchain.com/");

  • 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 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 ancoleman/ai-design-components at 76551b7b19.

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

Commit
76551b7b19
Manifest
skills/evaluating-llms/SKILL.md
Scanned
18 files, 8,883 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 AI agents to evaluate Large Language Models (LLMs) based on provided criteria. It assesses LLM responses for factors like helpfulness, accuracy, and harmlessness. The evaluation results can be used to compare different models or identify areas where a specific model needs improvement.

When to use it

  • Model Selection: Compare the performance of several LLMs on a given task before choosing one for deployment.
  • Prompt Engineering Iteration: Evaluate how changes to prompts affect an LLM's output quality and refine prompts accordingly.
  • Bias Detection: Assess LLMs for potential biases in their responses across different demographic groups or sensitive topics.
  • Performance Monitoring: Regularly evaluate deployed LLMs to ensure consistent performance over time.

Key capabilities

  • LLM response evaluation
  • Helpfulness assessment
  • Accuracy verification
  • Harmlessness checks
  • Comparative analysis of LLMs

Example prompts

  • "Evaluate the following LLM response: '[Response text]' based on helpfulness, accuracy, and harmlessness."
  • "Compare the responses from Model A and Model B to this prompt: '[Prompt text]'. Which model is better and why?"
  • “Assess this LLM output for potential biases: [Output Text]"

Tips & gotchas

The quality of the evaluation depends heavily on the clarity and specificity of the criteria provided. Consider providing detailed rubrics or examples to guide the evaluation process.