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Evaluate Benchmark Traces

Evaluate AI agent benchmark traces for data integrity, output quality, and efficiency.

32|4|Updated Feb 1, 2026
Search Tags:#code quality#data integrity#evaluation#ai agent#efficiency#benchmark#traces
One-click install
npx skills add https://github.com/sourcegraph/CodeScaleBench --skill evaluate-benchmark-traces
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Evaluate Benchmark Traces
Source: https://github.com/sourcegraph/CodeScaleBench/tree/main/skills/evaluate-traces
Command: npx skills add https://github.com/sourcegraph/CodeScaleBench --skill evaluate-benchmark-traces

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the comprehensive evaluation of AI agent benchmark run traces, ensuring data integrity, assessing output quality, and analyzing efficiency across various configurations and benchmarks.

Core Features & Use Cases

  • Data Integrity Audit: Validates MCP adoption, checks for baseline contamination, detects infrastructure failures, and ensures deduplication integrity.
  • Output Quality Assessment: Computes per-suite reward analysis, performs cross-config comparisons, and identifies task-level quality patterns.
  • Efficiency Analysis: Extracts token usage, wall clock time, MCP tool distribution, and cost-effectiveness metrics.
  • Use Case: After running a suite of AI coding agent benchmarks, use this Skill to generate a detailed report on which configurations performed best, identify common failure modes, and understand the cost-efficiency of different approaches.

Quick Start

Evaluate all official benchmark traces to generate a comprehensive report.

Frequently Asked Questions about Evaluate Benchmark Traces

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

FAQPage Schema
How do I evaluate AI agent benchmark traces for data integrity and output quality?▼

To evaluate AI agent benchmark traces, you audit data integrity by checking MCP adoption, baseline contamination, and deduplication, while assessing output quality through per-suite reward analysis and cross-config comparisons to identify task-level patterns.

What metrics are extracted during AI agent benchmark efficiency analysis?▼

AI agent benchmark efficiency analysis extracts token usage, wall clock time, MCP tool distribution, and cost-effectiveness metrics to determine the computational overhead and financial impact of different configurations.

How do I detect infrastructure failures and baseline contamination in benchmark runs?▼

Detecting infrastructure failures and baseline contamination in benchmark runs requires a comprehensive data integrity audit that validates MCP adoption and ensures deduplication integrity across the evaluated traces.

Can I compare different AI agent configurations using benchmark trace data?▼

Comparing different AI agent configurations is supported by cross-config comparisons and per-suite reward analysis, which identify task-level quality patterns to pinpoint which setups performed best.

What is the best way to analyze MCP tool distribution in agent benchmark traces?▼

Analyzing MCP tool distribution in agent benchmark traces is handled during the efficiency analysis phase, which extracts the distribution data alongside token usage and wall clock time to evaluate cost-effectiveness.

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