check-test-quality-go

name	check-test-quality-go
description	Analyze Go test files (*_test.go) for quality issues, anti-patterns, and code smells. This is a Go-specific tool that checks for external dependencies, complexity, flaky patterns, and testing anti-patterns. Use when users ask to check Go test quality, analyze Go tests for issues, find test anti-patterns in Go code, find flaky Go tests, or improve Go test reliability.
version	0.1.0

Check Go Test Quality

Purpose

Analyze Go test files to identify quality issues, anti-patterns, and code smells that make tests flaky, slow, complex, or unmaintainable. Provide actionable refactoring suggestions to improve test reliability and clarity.

Test quality encompasses:

• Reliability: Tests produce consistent results (not flaky)
• Speed: Tests run quickly without external dependencies
• Clarity: Tests are readable and maintainable
• Correctness: Tests verify behavior, not implementation details

When to Use This Skill

Use this skill when users ask to:

• Check test quality or analyze tests for issues
• Find flaky or unreliable tests
• Identify test anti-patterns or code smells
• Improve test maintainability
• Detect tests with external dependencies
• Find overly complex tests

Analysis Categories

This skill provides four Python scripts that analyze Go test files and output structured JSON. Each script focuses on a specific category of test quality issues.

1. External Dependencies (Critical)

Script: check-external-deps.py

Look for patterns indicating real external dependencies:

• Database connections (sql.Open, gorm.DB, connection strings)
• HTTP calls to real servers (http.Get, http.Post, http.Client{})
• Web servers on network ports (ListenAndServe, http.Server{})
• File I/O outside temp directories (os.Create, os.Open without t.TempDir())
• Time dependencies (time.Sleep - indicates flaky timing-based tests)

Why critical: External dependencies make tests slow, flaky, and environment-dependent. Tests fail in CI, cannot run offline, and cannot run in parallel safely.

Common fixes: Use mocks/fakes, httptest.Server for HTTP testing, test containers for integration tests, t.TempDir() for file operations, and replace time.Sleep with channels, WaitGroups, or require.Eventually.

See references/pattern-details.md for detailed pattern descriptions and fix examples.

2. Test Complexity (High)

Script: check-complexity.py

Analyze test structure for complexity indicators:

• Long test functions (>100 lines)
• Excessive setup code (>20 lines before first assertion)
• Too many mocks (>4 mock objects per test)
• Complex logic (multiple for, if, switch statements in tests)
• Poor test names (generic names like "TestFoo", "TestX")

Why it matters: Complex tests are hard to understand, maintain, and debug. Excessive mocking indicates coupling to implementation. Generic names provide no documentation value.

Common fixes: Extract setup to table-driven test helpers, reduce mocking by using real objects when simple, split complex tests into focused tests, use descriptive test names.

See references/pattern-details.md for detailed guidance and refactoring examples.

3. Flaky Patterns (Critical/High)

Script: check-flaky-patterns.py

Detect patterns causing non-deterministic test failures:

• time.Sleep() calls (timing-based synchronization)
• Goroutines without synchronization (go func() without WaitGroup/channels)
• Hardcoded timeouts (context.WithTimeout with fixed durations)
• Non-deterministic randomness (rand. without seeded source)
• Time-dependent assertions (time.Now() without mocking)
• Missing parallelization (tests that could use t.Parallel() but don't)

Why critical: Flaky tests fail intermittently, undermining trust in the test suite. Timing-based synchronization breaks on slower CI machines. Race conditions cause unpredictable failures.

Common fixes: Use channels/WaitGroups for async operations, mock time with fixed values, use seeded random generators, add synchronization primitives, enable t.Parallel() for independent tests.

See references/pattern-details.md for comprehensive flaky pattern examples.

4. Anti-Patterns (Medium)

Script: check-anti-patterns.py

Identify testing anti-patterns:

• Reflection accessing unexported fields (reflect., FieldByName, unsafe.Pointer)
• Over-verification of mocks (>5 EXPECT/ASSERT calls per test)
• Missing assertion messages (assert.Equal without descriptive messages)
• Too many assertions (>5 assertions per test)
• Global state modifications (package-level variables modified in tests)
• Missing cleanup (os.Setenv without defer or t.Setenv)

Why it matters: Testing unexported internals couples tests to implementation. Over-verifying mocks tests mock behavior, not actual behavior. Missing messages make failures hard to diagnose.

Common fixes: Test public API only, verify behavior/outcomes not mock sequences, add descriptive assertion messages, split tests with multiple assertions, use t.Setenv and t.Cleanup.

See references/pattern-details.md for anti-pattern details and solutions.

Workflow Instructions

Follow these steps when analyzing Go test quality:

Step 1: Verify Go Project

Confirm this is a Go project with test files:

# Check for go.mod
if [ ! -f "go.mod" ]; then
  echo "Error: Not a Go project (no go.mod found)"
  exit 1
fi

# Find test files
test_files=$(fd -e go -g '*_test.go' . 2>/dev/null || find . -name '*_test.go' 2>/dev/null)

if [ -z "$test_files" ]; then
  echo "No Go test files (*_test.go) found in this project"
  exit 0
fi

test_count=$(echo "$test_files" | wc -l)
echo "Found $test_count test files to analyze"

Step 2: Run Analysis Scripts

Execute all four scripts to gather comprehensive quality data. Run in parallel for speed:

# Parallel execution
uv run ${CLAUDE_SKILL_ROOT}/scripts/check-external-deps.py . > /tmp/external-deps.json &
PID1=$!

uv run ${CLAUDE_SKILL_ROOT}/scripts/check-complexity.py . > /tmp/complexity.json &
PID2=$!

uv run ${CLAUDE_SKILL_ROOT}/scripts/check-flaky-patterns.py . > /tmp/flaky.json &
PID3=$!

uv run ${CLAUDE_SKILL_ROOT}/scripts/check-anti-patterns.py . > /tmp/anti-patterns.json &
PID4=$!

# Wait for all to complete
wait $PID1 $PID2 $PID3 $PID4

Alternatively, run sequentially:

uv run ${CLAUDE_SKILL_ROOT}/scripts/check-external-deps.py .
uv run ${CLAUDE_SKILL_ROOT}/scripts/check-complexity.py .
uv run ${CLAUDE_SKILL_ROOT}/scripts/check-flaky-patterns.py .
uv run ${CLAUDE_SKILL_ROOT}/scripts/check-anti-patterns.py .

Step 3: Parse JSON Output

Each script outputs JSON in a consistent format. Collect and parse results:

# Extract all issues from all scripts
jq -s '[.[].issues[]]' /tmp/*.json > /tmp/all-issues.json

# Count by severity
jq '[.[] | select(.severity == "Critical")] | length' /tmp/all-issues.json

See references/json-schema.md for complete JSON schema documentation and parsing examples.

Step 4: Synthesize Findings

Combine results from all scripts and organize for presentation:

1. Deduplicate issues: Same file/line may appear in multiple scripts (e.g., time.Sleep detected by both external-deps and flaky-patterns)
2. Group by severity: Critical → High → Medium
3. Sort by file and line: Organize issues by location
4. Aggregate statistics: Total issues, files affected, severity breakdown

Deduplication logic:

# Remove duplicates (same file + line, keep highest severity)
jq 'unique_by([.file, .line])' /tmp/all-issues.json

Severity prioritization:

1. Critical: External dependencies, time.Sleep, race conditions
2. High: Complex tests (>100 lines), missing synchronization
3. Medium: Anti-patterns (reflection, over-mocking, global state)

Step 5: Generate Formatted Report

Present findings in a clear, actionable format organized by severity. Include:

• Summary statistics (total issues, breakdown by severity, files affected)
• Critical issues first (external dependencies, flaky patterns)
• High issues second (complexity)
• Medium issues third (anti-patterns)
• Code snippets showing the problem
• Impact explanation (why it matters)
• Suggested fixes with code examples
• Prioritized recommendations

See references/report-examples.md for complete report templates and examples.

Report structure:

## Test Quality Analysis Report

**Summary:**
- Total issues: [count]
- Critical: [count]
- High: [count]
- Medium: [count]
- Files with issues: [count] / [total] ([percentage]%)

---

## Critical Issues

### [file]:[line] - [test_name] [category]
**Issue:** [description]
**Code:** [snippet]
**Impact:** [why it matters]
**Suggested fix:** [solution with code]

---

## Recommendations

1. **Priority 1 (Critical):** [action items]
2. **Priority 2 (High):** [action items]
3. **Priority 3 (Medium):** [action items]

Example Usage

User query:

"Check my Go tests for quality issues"

Expected workflow:

1. Verify this is a Go project with test files
2. Run all 4 scripts in parallel
3. Collect and parse JSON outputs
4. Deduplicate issues (same file+line)
5. Group by severity (Critical, High, Medium)
6. Present formatted report with code context and suggestions

User query:

"Find flaky tests that might be failing intermittently"

Expected workflow:

1. Focus on check-flaky-patterns.py script
2. Highlight time.Sleep, goroutines without sync, and non-deterministic patterns
3. Explain why each pattern causes flakiness
4. Provide specific fixes (channels, WaitGroups, require.Eventually)

Error Handling

Handle these error cases gracefully:

Not a Go Project

if [ ! -f "go.mod" ]; then
  echo '{"error": "Not a Go project", "message": "No go.mod file found"}' | jq .
  exit 0  # Not an error, just not applicable
fi

No Test Files Found

test_files=$(fd -g '*_test.go' . 2>/dev/null)
if [ -z "$test_files" ]; then
  echo '{"error": "No tests found", "message": "No *_test.go files in project"}' | jq .
  exit 0
fi

Script Execution Failure

# Dependencies are automatically installed by uv
# If uv is not available, install it first:
if ! command -v uv &> /dev/null; then
  echo '{"error": "Missing dependency", "message": "uv not installed. Install from https://docs.astral.sh/uv/"}' | jq .
  exit 1
fi

Empty Results

If all scripts return zero issues, congratulate the user on having high-quality tests! Present a positive message highlighting good practices observed.

Additional Resources

• Pattern Details: references/pattern-details.md - Comprehensive documentation of all detected patterns with examples and fixes
• JSON Schema: references/json-schema.md - Complete JSON output format, field descriptions, and parsing examples
• Report Examples: references/report-examples.md - Full example reports showing different scenarios and output formats

Notes

• Scripts are stateless and can run in parallel
• Output is deterministic (sorted by file/line)
• Scripts use tree-sitter for accurate Go AST parsing
• Dependencies (tree-sitter, tree-sitter-go) are automatically installed by uv
• All paths are relative to project root
• Scripts work with any Go project structure