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coverage-analyzer

Analyze test coverage gaps and validate thresholds with pytest-cov.

25|2|Updated Oct 26, 2025
Search Tags:#testing#coverage#branch coverage#pytest-cov
One-click install
npx skills add https://github.com/matteocervelli/llms --skill coverage-analyzer-matteocervelli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: coverage-analyzer
Source: https://github.com/matteocervelli/llms/tree/main/.claude/skills/coverage-analyzer
Command: npx skills add https://github.com/matteocervelli/llms --skill coverage-analyzer-matteocervelli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill analyzes test coverage, identifies gaps, and generates actionable reports to improve quality.

Core Features & Use Cases

  • Coverage Measurement: Run tests with coverage and report missing lines.
  • Gap Identification: Highlight modules with low coverage and branching gaps.
  • Threshold Validation: Ensure coverage meets specified targets.

Quick Start

Run coverage analysis for a module and generate a detailed report with missing lines.

Frequently Asked Questions about coverage-analyzer

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

FAQPage Schema
How do I measure test coverage with pytest and identify untested code?▼

Test coverage measures which lines and branches your pytest tests execute. Use pytest-cov to run tests with coverage tracking, then generate reports showing missing lines and untested code paths. This reveals gaps in your test suite that could hide bugs.

What's the best way to enforce minimum coverage thresholds in my codebase?▼

Set coverage thresholds (e.g., >= 80%) and validate against them after each test run. Coverage analyzers check whether your actual coverage meets these targets and fail the build if thresholds are breached, preventing quality regression.

How can I identify which modules have the lowest test coverage?▼

Coverage gap analysis highlights modules and functions with low or zero coverage, ranking them by untested lines. This prioritizes where to add tests for maximum quality impact across your codebase or services.

Can I track coverage improvements over time and compare historical trends?▼

Yes. Multi-format reporting (HTML, XML, JSON) lets you store and compare coverage metrics across test runs, exposing trends in coverage growth or regressions and validating that test investments are paying off.

Does coverage analysis work for both unit and integration tests?▼

Coverage analysis applies to both unit and integration tests. It measures any code executed during pytest runs, supporting different test types and scopes from single modules to entire codebases.

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