coverage-analysis

Analyzes compiler-generated code coverage from fuzzing campaigns to identify blockers and harness gaps.

1|Updated Jan 31, 2026

Search Tags:#testing #code analysis #security #coverage #llvm #fuzzing #gcovr

Ready to run

npx skills add https://github.com/allanninal/claude-code-skills --skill coverage-analysis-allanninal

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: coverage-analysis
Source: https://github.com/allanninal/claude-code-skills/tree/main/skills/coverage-analysis
Command: npx skills add https://github.com/allanninal/claude-code-skills --skill coverage-analysis-allanninal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you understand which parts of your code are being exercised by your fuzzing harnesses and identify areas where the fuzzer might be stuck.

Core Features & Use Cases

Harness Effectiveness: Gauge how well your fuzzing harnesses are covering the target code.
Fuzzing Blockers: Identify "magic value checks" or other conditions that prevent the fuzzer from reaching new code paths.
Progress Tracking: Monitor coverage improvements over time as you refine harnesses or fuzzers.
Use Case: After running a fuzzer for several hours, you notice no new code paths are being discovered. Use this Skill to generate a coverage report, revealing that a specific input validation check is blocking the fuzzer, and then add the necessary values to your fuzzing dictionary.

Quick Start

Build your project with LLVM coverage instrumentation and then run the coverage analysis commands provided in the documentation.

Frequently Asked Questions about coverage-analysis

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

FAQPage Schema

How do I analyze code coverage to find fuzzing blockers?▼

Code coverage analysis identifies fuzzing blockers by highlighting exercised code paths and revealing conditions, like magic value checks, that prevent the fuzzer from reaching new areas. This helps pinpoint where the fuzzer is stuck.

How do I track fuzzing harness effectiveness using code coverage?▼

Harness effectiveness is tracked by generating coverage reports to gauge how well your fuzzing harnesses exercise the target code. Monitoring coverage improvements over time shows how effectively harness refinements reach new code paths.

Can I use llvm-cov and gcovr for C/C++ and Rust fuzzing coverage?▼

Yes, coverage analysis applies to C/C++, Rust, and other languages with compiler instrumentation support. It utilizes build tools like Clang or GCC and utilities like llvm-cov or gcovr to generate the required coverage reports.

What is the best way to generate a coverage report after a fuzzing campaign?▼

To generate a coverage report after a fuzzing campaign, build your project with LLVM coverage instrumentation using Clang or GCC. Then execute the provided coverage analysis commands to assess the results.

Why does my fuzzer stop finding new code paths in C/C++?▼

Your fuzzer stops finding new paths when specific input validation checks or magic values block progress. Coverage analysis reveals these stuck areas by showing which code branches remain unexercised during the fuzzing campaign.

Do I need compiler instrumentation for fuzzing coverage analysis?▼

Yes, compiler instrumentation is required for fuzzing coverage analysis. You must build your project using build tools like Clang or GCC with LLVM coverage instrumentation enabled to generate reports with utilities like llvm-cov.