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ai-ad-agents-test-runner

Statically infer PASS/FAIL/UNCERTAIN outcomes for agent unit tests without running pytest.

Updated Nov 8, 2025
Search Tags:#testing#quality assurance#static analysis#AI agents#pytest#unit test
One-click install
npx skills add https://github.com/wade56754/AI_ad_spend02 --skill ai-ad-agents-test-runner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-ad-agents-test-runner
Source: https://github.com/wade56754/AI_ad_spend02/tree/main/.claude/skills/ai-ad-agents-test-runner
Command: npx skills add https://github.com/wade56754/AI_ad_spend02 --skill ai-ad-agents-test-runner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solves?

Running full test suites for AI agents can be slow and resource-intensive during development. This Skill provides a fast, static analysis-based unit test simulation for agents/ related code, allowing developers to quickly infer test outcomes without actual execution.

Core Features & Use Cases

  • Static Test Simulation: Infers PASS/FAIL/UNCERTAIN outcomes by analyzing test and agent code, saving execution time.
  • Test Discovery: Automatically enumerates all test functions within the tests/agents/ directory.
  • Progress Tracking: Maintains test execution status (PENDING/FINISHED) through structured output, keeping you informed.
  • Use Case: During active development of an AI agent, use this Skill to quickly check if a new feature or bug fix has broken existing unit tests without needing to spin up a full test environment, accelerating your development cycle.

Quick Start

Use ai-ad-agents-test-runner in "DISCOVERY" mode to list all available agent tests.

Frequently Asked Questions about ai-ad-agents-test-runner

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

FAQPage Schema
How can I run unit tests for AI agents without executing pytest?▼

Static analysis infers PASS/FAIL/UNCERTAIN outcomes by analyzing test and agent code directly, eliminating the need to execute pytest. This approach saves time during development by avoiding full test environment setup while still validating test logic.

What's the fastest way to check if my agent code breaks existing tests during development?▼

Use static test simulation to enumerate all test functions in tests/agents/ and infer results without running them. This accelerates your feedback loop by providing immediate test status during active agent development.

Can I discover and track all agent unit tests without file I/O or shell commands?▼

Yes. The Skill automatically enumerates tests in tests/agents/ and maintains progress through structured output blocks tracking PENDING and FINISHED test status, all without file writes or system command execution.

How do I get deterministic test results for agents without relying on pytest execution?▼

Static analysis of test and source code produces deterministic PASS/FAIL/UNCERTAIN verdicts based on code inspection alone. Results are formatted as Markdown status blocks, enabling consistent test reporting without runtime variability.

What are the constraints when using static analysis for agent tests?▼

The Skill prohibits file writes, system command execution, and pytest runs. It works by analyzing code structure and logic statically, making it unsuitable for tests requiring actual I/O, network calls, or runtime side effects.

Can I run one agent test per invocation while tracking overall progress?▼

Yes. The Skill supports executing one test per invocation while maintaining progress state through structured blocks. This enables fine-grained test execution control and incremental progress tracking during development.

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