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harness-eval

Evaluate AI coding agents with 15 standardized software engineering tasks and quantitative scoring rubrics.

1|Updated May 21, 2026
Search Tags:#software engineering#quality assurance#benchmarking#metrics#evaluation#agent performance
One-click install
npx skills add https://github.com/hiddink-ai/hiddink-harness --skill harness-eval-hiddink-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harness-eval
Source: https://github.com/hiddink-ai/hiddink-harness/tree/main/templates/skills/harness-eval
Command: npx skills add https://github.com/hiddink-ai/hiddink-harness --skill harness-eval-hiddink-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the lack of standardized, quantitative evaluation for AI coding agents by providing a rigorous benchmark suite that measures task correctness and efficiency.

Core Features & Use Cases

  • Structured Benchmarking: Executes 15 predefined software engineering tasks ranging from API design to rate limiting.
  • Quantitative Scoring: Applies a weighted rubric across test coverage, architecture, error handling, and extensibility.
  • Efficiency Analysis: Integrates a 4-metric framework to compare agent trajectories, including step ratios and tool call efficiency.
  • Use Case: Use this skill to objectively compare the performance of different agent configurations or models before deploying them to production environments.

Quick Start

Invoke the harness-eval skill to run the full suite of 15 software engineering benchmarks and generate a comprehensive performance report.

Frequently Asked Questions about harness-eval

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

FAQPage Schema
How do I benchmark AI coding agent performance using standardized software engineering tasks?▼

You can evaluate AI coding agent performance by executing a suite of 15 standardized software engineering tasks and applying quantitative scoring rubrics to measure task correctness, efficiency, and structural integrity.

What metrics are used for evaluating agent trajectories and efficiency?▼

Agent efficiency analysis uses a 4-metric framework to compare agent trajectories, including step ratios and tool call efficiency, generating quantitative data to determine production readiness.

How do I objectively compare different AI agent configurations before production deployment?▼

You compare different agent configurations by running them against the structured benchmark suite and analyzing trajectory metrics with the quantitative scoring rubric to determine production readiness.

Can I use this benchmarking skill to measure test coverage and architecture quality?▼

Yes, the skill applies a weighted scoring rubric across test coverage, architecture, error handling, and extensibility to quantitatively measure structural integrity and code quality.

What is the best way to assess AI coding agent production readiness?▼

Assess production readiness by executing the 15 benchmark tasks and analyzing trajectory metrics alongside weighted rubric scores for structural integrity and task correctness.

Do I need specific test environments to run software engineering agent benchmarks?▼

Executing the benchmark suite requires an environment within the hiddink-harness ecosystem capable of running the 15 software engineering tasks and capturing trajectory metrics for analysis.

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