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Code Coverage With Gcov

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gadievron
Code Coverage with gcov

Add gcov code coverage instrumentation to C/C++ projects

Overview

Publishergadievron
Repositoryraptor
Skill nameCode Coverage with gcov
Stars
3.9K
Forks
581
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by gadievron on GitHub. Read the source before you install it.

Installation

Install the Code Coverage With Gcov AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/gadievron/raptor/tree/main/.claude/skills/crash-analysis/gcov-coverage
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gadievron/raptor.git /tmp/raptor
mkdir -p .claude/skills
cp -r /tmp/raptor/.claude/skills/crash-analysis/gcov-coverage .claude/skills/gadievron-code-coverage-with-gcov
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Coverage With Gcov in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Code Coverage With Gcov on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Code Coverage With Gcov is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Code Coverage with gcov

Purpose

Instrument C/C++ programs with gcov to measure test coverage.

Containment

The instrumented project is untrusted — its build scripts and the produced binary execute arbitrary code, and the .gcda/.gcno files that binary emits are untrusted bytes parsed by gcov/gcovr. Run every build, target-binary/test-suite execution, and gcov/gcovr invocation via libexec/raptor-run-sandboxed --output-dir <dir> <cmd> [args...] (--output-dir = the directory the command writes into). If a sandboxed step fails, fix the sandboxed invocation — never run the target's build system, binary, or the coverage extraction bare.

How It Works

Build with Coverage (sandboxed)

bash
libexec/raptor-run-sandboxed --output-dir <project-dir> gcc --coverage -o program source.c

Run Program (sandboxed)

bash
libexec/raptor-run-sandboxed --output-dir <project-dir> <project-dir>/program
# Creates .gcda files with execution data

Generate Reports

Text report (sandboxed — gcov parses the untrusted .gcda/.gcno bytes):

bash
libexec/raptor-run-sandboxed --output-dir <project-dir> gcov source.c
# Creates source.c.gcov with line-by-line coverage

HTML report (sandboxed):

bash
libexec/raptor-run-sandboxed --output-dir <project-dir> gcovr --html-details -o coverage.html

Coverage Flags

  • --coverage (shorthand for -fprofile-arcs -ftest-coverage -lgcov)
  • Add to both CFLAGS and LDFLAGS

Build System Integration

Makefile

makefile
ENABLE_COVERAGE ?= 0
ifeq ($(ENABLE_COVERAGE),1)
    CFLAGS += --coverage
    LDFLAGS += --coverage
endif

CMake

cmake
option(ENABLE_COVERAGE "Enable coverage" OFF)
if(ENABLE_COVERAGE)
    add_compile_options(--coverage)
    add_link_options(--coverage)
endif()

When User Requests Coverage

Steps

All build/run/report commands below go through libexec/raptor-run-sandboxed --output-dir <project-dir> ... (every make target — clean included — executes the untrusted Makefile's commands; rm -f *.gcda *.gcno is your own command and needs no wrapper):

  1. Detect build system (Makefile/CMake/other)
  2. Add --coverage to CFLAGS and LDFLAGS
  3. Clean previous build: libexec/raptor-run-sandboxed --output-dir <project-dir> make clean (or bare rm -f *.gcda *.gcno)
  4. Build with coverage: libexec/raptor-run-sandboxed --output-dir <project-dir> make ENABLE_COVERAGE=1 (or the cmake -DENABLE_COVERAGE=ON + build equivalent, same wrapper)
  5. Run tests: libexec/raptor-run-sandboxed --output-dir <project-dir> make test (or the project's test binary, same wrapper)
  6. Generate report: libexec/raptor-run-sandboxed --output-dir <project-dir> gcovr --html-details coverage.html --print-summary
  7. Present summary and path to HTML report

Output

Text (.gcov files):

        -:    0:Source:main.c
        5:   42:    int x = 10;
    #####:   43:    unused_code();
  • 5: = executed 5 times
  • #####: = not executed
  • -: = non-executable

HTML: Interactive report with color-coded coverage

Metrics

  • Line coverage: Executed lines / total lines
  • Branch coverage: Taken branches / total branches
  • Function coverage: Called functions / total functions

Target: 80%+ line coverage, 70%+ branch coverage

Frequently asked questions

What does the Code Coverage With Gcov AI skill do?

Add gcov code coverage instrumentation to C/C++ projects

Why use Code Coverage With Gcov on TypingMind?

Because you install it once and use it with any model. Code Coverage With Gcov is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Code Coverage With Gcov in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gadievron/raptor/tree/main/.claude/skills/crash-analysis/gcov-coverage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code Coverage With Gcov?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Code Coverage With Gcov?

As many as you like. As long as a model supports skills, you can use Code Coverage With Gcov with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Code Coverage With Gcov AI skill free?

It is published on GitHub by gadievron. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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