eval-driven-development

Name: eval-driven-development
Availability: InStock
Author: bennybennison

Define expected outcomes and run pass@k evals to validate agent behavior changes.

Updated Apr 11, 2026

Search Tags:#automation #testing #agent #regression #behavior #capability #eval

Ready to run

npx skills add https://github.com/bennybennison/agent-toolkit --skill eval-driven-development

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eval-driven-development
Source: https://github.com/bennybennison/agent-toolkit/tree/main/skills/eval-driven-development
Command: npx skills add https://github.com/bennybennison/agent-toolkit --skill eval-driven-development

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many teams modify agent rules, skills, or prompts without a reliable way to confirm that changes produce the intended behavior and do not introduce regressions.

Core Features & Use Cases

Eval Definition: Write an expected behavior test before making a change.
Baseline & Regression Checks: Run the eval against the current version to establish a pass/fail baseline, then re‑run after modifications.
Capability Validation: Confirm new capabilities pass a configurable pass@k threshold.
Use Cases: Adding a new rule, updating a prompt, or extracting a pattern via continuous learning where confidence in behavior is required.

Quick Start

Prompt the skill to create an eval for a new rule, run the baseline, apply the change, and verify the eval now passes.

Frequently Asked Questions about eval-driven-development

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

FAQPage Schema

How do I validate agent behavior changes before deploying prompt modifications?▼

You can validate agent behavior by defining an eval with expected outcomes and pass@k criteria before implementation, establishing a baseline, then re-running it after updates to confirm capability and regression performance.

What is eval-driven development for AI agents?▼

Eval-driven development is a testing approach where you define expected behavior outcomes and pass@k criteria before modifying agent rules, skills, or prompts to assess capability and prevent regressions.

How do I test for regressions when updating agent rules and skills?▼

You test for regressions by running an eval against the current agent version to establish a pass/fail baseline, applying your rule or skill updates, and re-running the eval to verify the expected outcomes still pass.

Can I use this eval approach for continuous learning pattern extraction?▼

Yes, you can apply this eval approach to continuous learning pattern extraction by defining expected behavior outcomes and pass@k criteria to ensure confidence in behavior consistency when new patterns are extracted.

What do I need to define before running an agent capability validation?▼

Before running agent capability validation, you need to define clear input scenarios, expected results, and pass@k criteria to assess whether the agent updates meet the required behavior consistency threshold.

What is the best way to ensure prompt modifications do not break existing agent capabilities?▼

The best way to ensure prompt modifications do not break existing capabilities is to establish an eval baseline before changes and verify the updated prompt still passes the configured pass@k threshold for expected outcomes.