ai-test-runner

Run Vitest suites against src/lib/ai/ and classify failures.

Updated Mar 31, 2026
One-click install
npx skills add https://github.com/nebuladevops/skills-engineering-auto --skill ai-test-runner-nebuladevops
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-test-runner
Source: https://github.com/nebuladevops/skills-engineering-auto/tree/main/ai-test-runner
Command: npx skills add https://github.com/nebuladevops/skills-engineering-auto --skill ai-test-runner-nebuladevops

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The AI layer test runner and builder automates the evaluation and generation of tests for proto_scribe's AI layer, diagnosing failures and proposing fixes without relying on external LLM calls, enabling safe, reproducible validation.

Core Features & Use Cases

  • Runner mode runs the Vitest suite against src/lib/ai/ to diagnose failures and categorize root causes.
  • Builder mode (--build) analyzes source files and outputs a prioritized plan for fully runnable Vitest tests.
  • Strict scope and safety constraints ensure deterministic execution, with explicit diffs rather than automatic changes.

Quick Start

Install the skill and run /ai-test to diagnose tests, or /ai-test --build to generate a test plan.

Frequently Asked Questions about ai-test-runner

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

FAQPage Schema
How do I diagnose Vitest failures in my AI layer without making real LLM calls?

The AI test runner executes your Vitest suite against the src/lib/ai/ directory, categorizes failure root causes, and outputs structured diffs with proposed fixes. It enforces deterministic behavior and strict isolation to ensure reproducible validation without external LLM calls.

Can I generate a test plan for AI source files using Vitest?

Yes, you can generate a prioritized Vitest test plan by running the builder mode. It analyzes your AI source files and outputs a structured, prioritized plan for fully runnable tests, allowing you to build comprehensive AI layer validation without external LLM dependencies.

Why do my AI test suites fail when external LLM calls are involved?

AI test suites often fail due to non-deterministic behavior from external LLM calls. This runner enforces isolation by bypassing real LLM interactions, categorizing the root causes of failures, and proposing fixes through structured diffs to guarantee safe, reproducible validation.

Does the AI test runner apply code fixes automatically?

No, the AI test runner does not apply fixes automatically. It outputs explicit structured diffs for your review, maintaining strict safety constraints and deterministic execution while ensuring you retain full control over applying the proposed changes to your AI layer.

What are the limitations of using automated test runners for AI layer validation?

Automated AI layer test runners are limited to specific scopes like src/lib/ai/ and enforce strict isolation to prevent real LLM calls. They propose fixes via structured diffs but cannot execute changes automatically, requiring manual review for safe, deterministic validation.