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test-coverage

Generate comprehensive test coverage reports for Python, JavaScript, Go & Java projects.

21|Updated Nov 7, 2025
Search Tags:#code quality#unit tests#testing#integration tests#QA#test coverage#software reliability
One-click install
npx skills add https://github.com/mehdic/bazinga --skill test-coverage
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: test-coverage
Source: https://github.com/mehdic/bazinga/tree/main/.claude/skills/test-coverage
Command: npx skills add https://github.com/mehdic/bazinga --skill test-coverage

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest, pytest-cov, jest, jacoco, and includes scripts (resource) components.

What problem does it solve?

Untested code paths are a major source of bugs and production incidents. This Skill automatically generates comprehensive test coverage reports, highlighting areas of the codebase that lack sufficient testing, helping you prevent regressions and improve software reliability, so you can deploy with confidence.

Core Features & Use Cases

  • Multi-Language Coverage: Supports Python (pytest-cov), JavaScript (Jest), Go (go test -cover), and Java (JaCoCo).
  • Detailed Reports: Provides overall coverage percentages, per-file breakdowns, and lists of uncovered lines and critical paths.
  • Threshold Flagging: Automatically identifies files and sections of code that fall below predefined coverage thresholds.
  • Use Case: Before merging a pull request, run this Skill to verify that new features and bug fixes have adequate test coverage. It helps maintain high code quality standards and ensures that critical business logic is thoroughly tested.

Quick Start

To generate a test coverage report for the current project: bash .claude/skills/test-coverage/coverage.sh

Frequently Asked Questions about test-coverage

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

FAQPage Schema
How do I generate test coverage reports for my codebase?▼

Test coverage reports measure what percentage of your code is executed by tests. This Skill automatically generates comprehensive coverage reports using language-specific tools (pytest-cov for Python, Jest for JavaScript, go test -cover for Go, JaCoCo for Java), showing overall percentages, per-file breakdowns, and uncovered lines to help you identify gaps before deployment.

Can I use test coverage tools across Python, JavaScript, Go, and Java projects?▼

Yes, this Skill supports multiple languages with their respective coverage tools: pytest-cov for Python, Jest for JavaScript, go test -cover for Go, and JaCoCo for Java. It auto-installs required tools and generates unified JSON reports, so you can run coverage analysis consistently across a polyglot codebase.

When should I run test coverage analysis in my code review process?▼

Run coverage analysis before approving pull requests, especially when developers claim added tests or implement new features and bug fixes. This ensures critical business logic is thoroughly tested, identifies untested code paths that could cause bugs, and maintains high code quality standards before merging.

What does a test coverage report include?▼

Coverage reports provide overall coverage percentages, per-file breakdowns showing which files meet or fall below thresholds, lists of uncovered lines, and identification of critical paths lacking tests. The Skill outputs results as JSON at bazinga/artifacts/{SESSION_ID}/skills/coverage_report.json for automated analysis.

How do I identify code sections that need more testing?▼

This Skill automatically flags files and code sections falling below predefined coverage thresholds, highlighting which areas lack sufficient testing. The detailed per-file breakdown and list of uncovered lines help you prioritize where to add tests for maximum impact on code reliability.

Why is test coverage important before deploying code?▼

Untested code paths are a major source of production bugs and incidents. Test coverage analysis ensures new features and fixes have adequate testing, reveals risky gaps in logic, and lets you deploy with confidence that critical functionality is thoroughly validated.

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