LLM Evaluation Harness

LLM Evaluation Harness is an AI Engineering skill for Claude Code, published by OpenRaiser in NanoResearch.

1.4K stars95 forkson OpenRaiser/NanoResearchAdded 2026/08/19Repository updated 2026/05/26

agent-skillsagentsaiai-agentsai-scientistartificial-intelligenceautonomous-agentsautonomous-researchautoresearchclaude-codeclaude-skillsnanobotopenclaw

Install in seconds

Install LLM Evaluation Harness

Copy LLM Evaluation Harness into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/lm-evaluation-harness ~/.claude/skills/lm-evaluation-harness

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/OpenRaiser/NanoResearch.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/vendor-ai-research/lm-evaluation-harness/SKILL.md in OpenRaiser/NanoResearch
Installs to: ~/.claude/skills/lm-evaluation-harness
Collection: One of 16 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What LLM Evaluation Harness does

LLM Evaluation Harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics. Use when benchmarking model quality, comparing models, or tracking training progress.

LLM Evaluation Harness is cataloged under AI Engineering on DirSkills. LLM Evaluation Harness comes from a repository tagged agent-skills, agents, ai, ai-agents and ai-scientist.

Documentation

README

Excerpt

lm-evaluation-harness - LLM Benchmarking

Quick start

lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.

Installation:

pip install lm-eval

Evaluate any HuggingFace model:

lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k,hellaswag \
  --device cuda:0 \
  --batch_size 8

View available tasks:

lm_eval --tasks list

Common workflows

Workflow 1: Standard benchmark evaluation

Evaluate model on core benchmarks (MMLU, GSM8K, HumanEval).

Copy this checklist:

Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model
- [ ] Step 3: Run evaluation
- [ ] Step 4: Analyze results

This is the opening of the README. Read the full README on GitHub.

Frequently asked about LLM Evaluation Harness

What else does OpenRaiser publish alongside LLM Evaluation Harness?
LLM Evaluation Harness is one of 16 skills that DirSkills catalogs from OpenRaiser/NanoResearch, the repository it ships in. Its siblings there include Academic Plotting, Autoresearch and Creative Thinking for Research. Each one is a separate skill with its own page in this directory, installs the same way LLM Evaluation Harness does, and is maintained by OpenRaiser in that same repository. The rest of the collection is listed on the OpenRaiser/NanoResearch page.
How does LLM Evaluation Harness compare to other AI Engineering skills?
LLM Evaluation Harness ranks #1076 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of LLM Evaluation Harness against them. Open each page to compare what they document and how they install.