home / skills / willsigmon / sigstack / ai-test-gen-expert

ai-test-gen-expert skill

/plugins/testing/skills/ai-test-gen-expert

This skill automatically generates and improves tests using AI tooling like mabl and qodo-cover to boost coverage and reduce maintenance.

npx playbooks add skill willsigmon/sigstack --skill ai-test-gen-expert

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SKILL.md
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---
name: AI Test Generation Expert
description: AI-powered test generation - mabl, Qodo Cover, automated coverage improvement
allowed-tools: Read, Edit, Bash, WebFetch
model: sonnet
---

# AI Test Generation Expert

Leverage AI to automatically generate and improve tests.

## Top Tools (2026)

### mabl
- AI-driven test creation (2x faster)
- Self-healing tests
- Web, mobile, API testing
- Starting ~$500/mo (custom pricing)

### Qodo Cover
- Open source test generation
- Targets coverage gaps
- GitHub Actions integration
- Free CLI tool

### Testim (Tricentis)
- AI-powered element detection
- Codeless + code options
- Enterprise focused

### Virtuoso QA
- Natural language test creation
- 85% maintenance reduction
- Self-healing automation

## Qodo Cover Setup

```bash
# Install
pip install qodo-cover

# Run on file
qodo-cover \
  --source-file src/calculator.ts \
  --test-file tests/calculator.test.ts \
  --code-coverage-report coverage/lcov.info

# GitHub Action
- uses: qodo-ai/qodo-cover-action@v1
  with:
    project-language: typescript
    source-file: src/calculator.ts
```

## AI Test Generation Patterns

### Coverage Gap Targeting
```
1. Run existing tests
2. Collect coverage report
3. AI analyzes uncovered lines
4. Generate tests for gaps
5. Validate and merge
```

### Mutation Testing
```
1. AI generates code mutations
2. Tests run against mutants
3. Find tests that don't catch bugs
4. Generate missing assertions
```

### Property-Based Generation
```typescript
// AI generates property tests
test.prop([fc.integer(), fc.integer()])('addition commutes', (a, b) => {
  expect(add(a, b)).toBe(add(b, a));
});
```

## Best Practices

1. **Start with coverage report**
   - Know what's untested
   - Prioritize critical paths

2. **Review generated tests**
   - AI tests need human review
   - Ensure meaningful assertions
   - Check edge cases

3. **Integrate in CI**
   - Run coverage checks
   - Fail on coverage drops
   - Generate tests for new code

4. **Combine with existing tests**
   - Don't replace human tests
   - Augment coverage gaps
   - Learn from existing patterns

## Coverage Goals
- 80% line coverage baseline
- 90% for critical paths
- 100% for security functions

Use when: Improving test coverage, generating edge case tests, reducing testing debt

Overview

This skill helps teams generate and improve automated tests using AI-driven tools and patterns. It focuses on closing coverage gaps, creating property and mutation tests, and integrating generated tests into CI. The goal is faster, targeted coverage improvement while preserving human review and quality control.

How this skill works

The skill analyzes existing test runs and coverage reports, identifies uncovered lines and risky code paths, and uses AI to propose or generate tests that exercise those gaps. It supports tools like mabl, Qodo Cover, Testim, and Virtuoso QA, and can produce unit, property-based, and mutation-targeted tests. Generated tests are validated locally and prepared for CI or pull request workflows so teams can review and merge with confidence.

When to use it

  • Boost overall or targeted code coverage after feature development
  • Automatically generate edge-case and property-based tests
  • Find weak spots using mutation testing and create assertions
  • Integrate test generation into CI to prevent coverage regressions
  • Supplement human-written tests to reduce maintenance debt

Best practices

  • Begin with an up-to-date coverage report and prioritize critical paths
  • Review and edit AI-generated tests to ensure meaningful assertions and readability
  • Run mutation testing to reveal brittle or missing checks and let AI propose fixes
  • Integrate generation into CI but gate merges behind human review and coverage thresholds
  • Combine AI tests with existing suites; use them to augment—not replace—human tests

Example use cases

  • Run Qodo Cover in GitHub Actions to auto-generate tests for uncovered TypeScript modules
  • Use property-based generation to validate invariants like commutativity or idempotence
  • Apply mutation testing to discover fragile tests, then auto-generate assertions to catch mutants
  • Set up mabl or Testim for end-to-end flows and leverage self-healing tests to reduce maintenance
  • Create a repeatable pipeline: run tests, collect coverage, generate tests for gaps, validate, and open PRs

FAQ

Will AI-generated tests replace my existing tests?

No. Generated tests are intended to augment existing suites. Human review ensures assertions are meaningful and matches project standards.

How do I keep generated tests from failing flakily in CI?

Validate generated tests locally, use stable selectors or mocks for external dependencies, and add flaky-detection steps in CI. Prefer property or unit tests for deterministic results.