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Evaluating Llms Harness

Checked
zechenzhangagi·vlatest·ai-ml·MIT

This skill assesses LLM performance within a harness environment, providing insights for optimization and ensuring consistent results across 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 orchestra-research/ai-research-skills --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

zechenzhangagi

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Install target

Live repository

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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.

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  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • Pinned to a commit: passed

    Scanned zechenzhangagi/ai-research-skills at 773a52944b.

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Commit
773a52944b
Manifest
11-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.
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About this skill

What it does

This skill, zechenzhangagi-evaluating-llms-harness, provides a framework for evaluating Large Language Models (LLMs). It allows users to systematically assess LLM performance across various metrics. The harness facilitates standardized testing and comparison of different models or model versions.

When to use it

  • Benchmarking new LLMs: Quickly compare the capabilities of newly released language models against existing ones.
  • Tracking Model Improvements: Monitor changes in performance after fine-tuning or retraining an LLM.
  • Identifying Strengths & Weaknesses: Pinpoint areas where a model excels and areas needing improvement.
  • Comparing different prompting strategies: Evaluate how various prompt designs impact the same LLM's output quality.

Key capabilities

  • LLM evaluation framework
  • Standardized testing
  • Performance comparison
  • Model version tracking

Example prompts

  • "Evaluate the performance of Model A on the summarization task."
  • "Compare Model B and Model C’s ability to answer complex reasoning questions."
  • “Run a benchmark test suite against this LLM.”

Tips & gotchas

This skill requires familiarity with LLM evaluation methodologies. The results are only as good as the quality of the evaluation data used; ensure your datasets are representative and unbiased.