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Evaluating Llms Harness

Checked
davila7·vlatest·ai-ml·MIT

This skill assesses LLM performance within a harness, providing insights into accuracy and efficiency for optimized deployments.

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 davila7/claude-code-templates --skill evaluating-llms-harness -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

davila7

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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/evaluation-lm-evaluation-harness/SKILL.md
Scanned
5 files, 2,272 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

The evaluating-llms-harness skill provides a framework for systematically assessing Large Language Model performance across various tasks. It enables users to define custom evaluation criteria, generate test cases, and analyze output quality with structured metrics.

When to use it

  • Validating the accuracy and relevance of AI-generated code snippets before deployment.
  • Benchmarking different model responses against a specific set of business requirements or constraints.
  • Automating regression testing for prompts that have changed over time.
  • Creating reproducible reports on model behavior for stakeholder reviews.

Key capabilities

  • Structured evaluation pipelines for consistent scoring.
  • Customizable test case generation tailored to specific domains.
  • Automated analysis of output quality and adherence to instructions.
  • Integration with existing AI agent workflows for continuous feedback loops.

Example prompts

  • "Run the evaluating-llms-harness on my latest coding prompt to check for logic errors and edge cases."
  • "Generate a new set of test cases using this harness to evaluate how well the model handles ambiguous user requests."
  • "Compare the output of two different models using the metrics defined in this evaluation harness."

Tips & gotchas

Ensure your evaluation criteria are specific enough to yield meaningful scores rather than vague generalizations. This harness works best when paired with clear, deterministic test cases that have known correct answers or strict formatting requirements.