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name: test-health-monitor description: | Intelligent agent that monitors, analyzes, and improves test suite health. Detects flaky tests, identifies coverage gaps, tracks test performance trends, and recommends improvements. Ensures test suites remain valuable and maintainable over time. license: MIT allowed-tools:

• Bash
• Read
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• Grep compatibility: claude-code: ">=1.0.0" metadata: version: "1.0.0" author: "QuantQuiver AI R&D" category: "testing" tags:
  • test-health
  • flaky-tests
  • test-coverage
  • test-maintenance

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Test Health Monitor

Purpose

Intelligent agent that monitors, analyzes, and improves test suite health. Detects flaky tests, identifies coverage gaps, tracks test performance trends, and recommends improvements. Ensures test suites remain valuable and maintainable.

Triggers

Use this skill when:

• "analyze test suite health"
• "find flaky tests"
• "improve test coverage"
• "why are tests slow"
• "test maintenance report"
• "optimize test suite"
• "fix broken tests"
• "test health check"

When to Use

• CI/CD pipeline slowdowns
• Intermittent test failures
• Declining test coverage
• Test maintenance burden increasing
• New team members struggling with tests
• Tests that no one trusts

When NOT to Use

• Writing new tests (use unit-test-generator)
• Running tests (use CI/CD directly)
• Testing specific functionality (use appropriate test skill)

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Core Instructions

Health Monitoring Dimensions

Health Metrics

Flaky Test Detection

A test is considered flaky when:

• Same code produces different results
• Passes in some runs, fails in others
• Failure rate between 2% and 98%

Common Causes:

• Race conditions
• External service dependencies
• Time-dependent logic
• Resource contention
• Network latency

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Templates

Health Report

Copy# Test Suite Health Report

**Report ID:** {report_id}
**Generated:** {generated_at}
**Analysis Period:** {period_days} days

## Health Score

### Overall: {overall_score}/100

| Dimension | Score | Status |
|-----------|-------|--------|
| Reliability | {reliability} | {status} |
| Coverage | {coverage} | {status} |
| Performance | {performance} | {status} |
| Maintainability | {maintainability} | {status} |

## Summary

| Metric | Value |
|--------|-------|
| Total Tests | {total_tests} |
| Passing | {passing_tests} |
| Flaky | {flaky_tests} |
| Slow Tests | {slow_tests} |

## Flaky Tests

| Test | Flaky Rate | Runs | Last Failure |
|------|------------|------|--------------|
| `{test_name}` | {flaky_rate}% | {runs} | {last_failure} |

## Recommendations

### {title}

**Priority:** {priority}
**Impact:** {impact}

{description}

**Steps:**
1. {step}

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Example

Input: Analyze 30 days of CI history for flaky tests

Output:

Copy## Health Score

### Overall: 72.3/100

| Dimension | Score | Status |
|-----------|-------|--------|
| Reliability | 65.0 | Warn |
| Coverage | 78.5 | Warn |
| Performance | 75.0 | Warn |
| Maintainability | 85.0 | Pass |

## Flaky Tests

| Test | Flaky Rate | Runs | Last Failure |
|------|------------|------|--------------|
| `test_api_timeout` | 15.2% | 125 | 2025-01-10 |
| `test_database_connection` | 8.3% | 96 | 2025-01-11 |

### Suspected Causes

**test_api_timeout:**
- Network latency or slow external dependencies
- High timing variance (possible race condition)

## Recommendations

### Fix Critical Flaky Tests

**Priority:** Critical
**Impact:** Reduce CI failures by 50-80%

2 tests fail >10% of the time, causing CI unreliability

**Steps:**
1. Review failure patterns for each test
2. Add retry logic or increase timeouts
3. Mock flaky external dependencies
4. Add deterministic test data

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Validation Checklist

• Sufficient CI history analyzed (>= 10 runs per test)
• Coverage data is current
• Flaky tests have suspected causes
• Performance baseline established
• Recommendations are actionable
• Priority reflects business impact
• Effort estimates are realistic

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Related Skills

• unit-test-generator - For generating new tests
• api-contract-validator - For API test coverage
• security-test-suite - For security test coverage
• data-validation - For data quality test patterns