eval-harness

Defines and runs eval-driven development criteria with pass/fail graders and pass@k metrics.

Updated Mar 26, 2026
One-click install
npx skills add https://github.com/inuishan/PET --skill eval-harness-inuishan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/inuishan/PET/tree/main/.codex/skills/eval-harness
Command: npx skills add https://github.com/inuishan/PET --skill eval-harness-inuishan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI-assisted coding sessions often lack objective success criteria, making it hard to know whether an agent's output actually works or whether a prompt change broke existing behavior. This Skill introduces eval-driven development (EDD), treating evals as unit tests for AI workflows so you can define pass/fail criteria before implementation and measure reliability over time. ## Core Features & Use Cases - Capability and Regression Evals: Define structured eval templates that test new agent abilities and guard existing functionality against regressions. - Three Grader Types: Use deterministic code-based graders (grep, npm test, build checks), model-based graders for open-ended outputs, and human review flags for high-risk changes. - Reliability Metrics: Track pass@k (at least one success in k attempts) and pass^k (all k trials succeed) to benchmark agent performance across model versions. - Use Case: Before adding an authentication feature, define evals for registration, login, and session handling, then run /eval check add-authentication during implementation and generate a final report showing 5/5 capability evals passed with pass@3 at 100%. ## Quick Start Ask the agent to define an eval-driven development plan with pass/fail criteria for the feature you are about to implement.

Frequently Asked Questions about eval-harness

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

FAQPage Schema
How do I set up eval-driven development for AI coding agents?▼

Define capability and regression evals before writing code, specifying success criteria for each task. Run evals during implementation with code-based graders like grep or npm test, then generate a report tracking pass@k metrics.

What is the difference between pass@k and pass^k metrics?▼

pass@k measures whether at least one of k attempts succeeds, with pass@3 above 90% as a typical target. pass^k requires all k trials to succeed, a stricter bar used for critical paths and regression evals.

How do I write a regression eval for prompt or agent changes?▼

Record a baseline SHA or checkpoint, list existing tests with PASS/FAIL status, and compare results against the previous run. A regression eval reports X/Y passed versus the baseline to detect broken functionality.

When should I use a model-based grader instead of a code-based grader?▼

Use code-based graders for deterministic checks like build success or test passes. Use model-based graders for open-ended outputs requiring judgment on structure, edge cases, and error handling, scored on a 1-5 scale.

Should security checks be fully automated in evals?▼

No, security-sensitive changes should be flagged for human review with a risk level of LOW, MEDIUM, or HIGH. The framework explicitly recommends never fully automating security checks.