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benchmark-runner

Automate AI skill benchmarking against a baseline in isolated sessions.

93|23|Updated Mar 4, 2026
Search Tags:#benchmarking#Claude Code#GitHub#AI performance#skill evaluation
One-click install
npx skills add https://github.com/RConsortium/pharma-skills --skill benchmark-runner-rconsortium
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-runner
Source: https://github.com/RConsortium/pharma-skills/tree/main/_automation/benchmark-runner
Command: npx skills add https://github.com/RConsortium/pharma-skills --skill benchmark-runner-rconsortium

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Claude Code CLI, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the benchmarking of AI skills to measure their performance against a baseline, enabling consistent evaluation and comparison.

Core Features & Use Cases

  • Benchmarking: Automates the benchmarking process for AI skills.
  • Performance Measurement: Measures accuracy, efficiency, and consistency of AI skills.
  • Use Case: For evaluating the impact of skill improvements on model performance.

Quick Start

Enable the benchmark-runner skill and provide the desired evaluation case.

Frequently Asked Questions about benchmark-runner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate AI skill benchmarking to measure performance against a baseline?▼

AI skill benchmarking is automated by running matched isolated sessions that perform "fair fight" comparisons against a baseline. This process measures accuracy, efficiency, and consistency to evaluate the impact of skill improvements on model performance.

What is a fair fight comparison when evaluating AI performance?▼

A fair fight comparison is a benchmarking method that evaluates AI skills against a baseline using matched isolated sessions. This ensures consistent evaluation by measuring accuracy, efficiency, and consistency across identical test conditions.

Do I need the Claude Code CLI to run skill evaluation benchmarks?▼

Yes, the Claude Code CLI is required to run skill evaluation benchmarks. The benchmarking process depends on the CLI to execute matched isolated sessions and post scored results directly to the linked GitHub issue.

How do I post scored benchmark results to a GitHub issue?▼

Scored benchmark results are posted to a GitHub issue automatically after the benchmarking process completes. The skill evaluates AI performance in matched isolated sessions and updates the linked issue with the final comparison scores.

When should I use automated benchmarking for AI skills?▼

Automated benchmarking should be used when evaluating the impact of skill improvements on model performance. It provides consistent evaluation by measuring accuracy, efficiency, and consistency against a baseline without manual intervention.

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