How do I install the Exps Performance skill?

Run npx skillmds@latest add majiayu000/exps-performance in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Exps Performance skill do?

Documentation for the main LLM performance experiments (exps_performance) It is listed under AI & ML on SkillMD.

Is Exps Performance safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Exps Performance?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Exps Performance free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Exps Performance?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Exps Performance by majiayu000 — Agent Skill

Name: Exps Performance
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

File contents

exps_performance - Main Performance Experiments

Purpose

Run LLM inference on algorithmic problems, collect CoT rationales and evaluate correctness.

Key Files

File	Purpose
`src/exps_performance/main.py`	Main runner
`src/exps_performance/dataset.py`	Dataset definitions
`src/exps_performance/arms.py`	Evaluation arms (NL, code_sim, code_exec)
`src/exps_performance/llm.py`	LLM interface
`src/exps_performance/analysis.py`	Analysis and plotting
`src/exps_performance/noise.py`	Noise injection experiments

Results Structure

src/exps_performance/results/
  {model}_seed{seed}/
    tb/
      run_{timestamp}/
        res.jsonl  # Main results file

res.jsonl Schema

Field	Description
`kind`	Problem type (e.g., "knap", "bellman_ford")
`digit`	Problem size/digits
`nl_correct`	NL evaluation result
`code_correct`	Code execution result
`sim_correct`	Code similarity result
`model`	Model name
`seed`	Random seed

Data Sources

Performance results: src/exps_performance/results/{model}_seed{seed}/tb/run_*/res.jsonl
Excludes gsm8k from accuracy calculations in logistic analysis

Running Commands

# Run main experiment
uv run python src/exps_performance/main.py --model <model> --seed <seed>

# Run analysis
uv run python src/exps_performance/analysis.py

Frequently asked questions

Exps Performance