Audit Your Test Suite in 16 Dimensions with This Agent Skill

Where this fits

You inherit a codebase with thousands of tests, but you can't tell if they actually protect your product. Or you're about to ship a release and need confidence that your test infrastructure isn't silently rotting. The ai-assist-test-audit skill gives your agent a structured, repeatable way to evaluate a test suite across 16 dimensions—coverage, quality, mocking, data management, CI/CD, performance, mutation testing, and modern patterns—and returns a severity-ranked report with a health score and remediation plan.

Why agents benefit

• Depth control – Run a quick scan, a standard audit, or a deep dive, so the agent adapts to your time budget and risk tolerance.
• Deterministic metrics – Instead of vague opinions, the skill produces concrete numbers and a gap matrix you can act on.
• Health scoring – A single 0–100 score makes it easy to track improvement over time or compare different projects.
• Actionable remediation – The output includes prioritized fixes, not just problems, so your agent can immediately start suggesting changes.
• Context awareness – It respects AGENTS.md conventions and spec files, aligning the audit with your project's actual standards.

Practical scenarios

Pre-release health check

Before a major deployment, you ask your agent to run a standard audit on the test suite. It flags a critical gap in integration test coverage for the payment module and a data cleanup issue in your fixture setup. You fix those before they become production incidents.

CI/CD integration

You add a quick audit step to your CI pipeline. Every pull request runs a 30-second scan that catches regressions in test quality—like a test that stopped asserting its core behavior—before they merge.

Legacy codebase onboarding

A new team member uses the skill to understand the existing test infrastructure. The deep audit generates a map of what's covered, what's mocked, and where the brittle spots are, turning a confusing mess into a clear roadmap.

Add it to your agent workflow

Install the skill with:

npx skills add jpweb/ai-assist-skills --skill ai-assist-test-audit

Then ask your agent to run it, specifying depth and scope:

# Example prompt to your agent
Run a standard test audit on the ./backend directory, focusing on coverage and mocking.

The agent will respond with a step-by-step audit, starting with 🩺 [Test Audit Step 1: ...], and produce a final report with a health score and gap matrix.

Compare before adopting

Before committing to this skill, compare it with alternatives like simple coverage tools (e.g., c8), mutation testing frameworks (e.g., Stryker), or manual code review checklists. Look for:

• Quality signals – Does the skill produce actionable, non-generic output? Try it on a small project first.
• Maintenance freshness – Check the GitHub repo for recent commits and issue responses. The skill is at 88 stars and actively maintained.
• Alternatives – Some CI platforms have built-in test analytics, but they rarely cover all 16 dimensions in one pass.
• Workflow fit – Does the skill's output integrate with your existing agent's tools and reporting style? The depth control is a differentiator.

Why it is worth tracking

This skill addresses a real pain point: test suites that look comprehensive but hide critical gaps. Its deterministic scoring and remediation focus make it a practical addition to any agent's toolkit. The project has steady community momentum (88 stars and an active author), and the skill is part of a broader collection of ai-assist skills, so it's likely to evolve with new testing patterns. Track it if you regularly evaluate code quality or want your agent to provide more than just coverage percentages. For a full list of similar skills, see OpenAgentSkill and consider adding it to your next agent workflow.