Does this skill help with testing the fixes?

Yes, it follows a Red-to-Green pattern: it writes a failing test to reproduce the bug, applies a minimal fix, and then verifies that the test passes with evidence logs.

How does Bug Review identify specific languages?

The skill analyzes project manifest files such as Cargo.toml, package.json, or pyproject.toml to determine the language ecosystem and apply the correct expertise persona.

Can I use this for security audits?

Absolutely. Its systematic approach to identifying logic errors, API misuse, and concurrency issues makes it an excellent tool for deep security and code quality reviews.

What kind of evidence does the skill provide for bugs?

It generates detailed reports including precise file/line references, severity levels, execution logs from reproduction commands, and impact assessments.

Bug Review Workflow

Name: Bug Review Workflow
Author: athola

byathola

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Security & Testing

Systematically identifies, documents, and fixes software defects using language-specific expertise and evidence-based verification.

About

The Bug Review skill provides a structured framework for Claude to perform deep-dive bug hunting and defect resolution within any codebase. It moves beyond simple code analysis by detecting the specific language environment, creating reproducible test plans, and generating idiomatic patches. By enforcing a rigorous workflow that includes severity classification and a 'Red-to-Green' verification process, this skill ensures that every fix is robust, minimal, and fully validated before deployment.

Key Features

Comprehensive risk assessment and verification reporting
Evidence-based defect logging with severity and root cause analysis
Systematic reproduction planning with native test suite integration
Idiomatic fix generation following language-specific best practices
Automatic language and version detection via manifest analysis
10 GitHub stars

Use Cases

Conducting deep bug hunts and security audits before major software releases
Investigating and resolving production issues using reproducible evidence
Refactoring legacy code to fix latent logic errors and improve code quality

About

Key Features

Comprehensive risk assessment and verification reporting
Evidence-based defect logging with severity and root cause analysis
Systematic reproduction planning with native test suite integration
Idiomatic fix generation following language-specific best practices
Automatic language and version detection via manifest analysis
10 GitHub stars

Use Cases

Conducting deep bug hunts and security audits before major software releases
Investigating and resolving production issues using reproducible evidence
Refactoring legacy code to fix latent logic errors and improve code quality