make-github-issues
Review codebase and create GitHub issues for identified problems
Best use case
make-github-issues is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Review codebase and create GitHub issues for identified problems
Teams using make-github-issues should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/make-github-issues/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How make-github-issues Compares
| Feature / Agent | make-github-issues | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Review codebase and create GitHub issues for identified problems
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
Review codebase and create GitHub issues for identified problems: $ARGUMENTS
Follow these steps:
1. **Analyze the codebase**:
- If $ARGUMENTS specifies files/directories, focus on those areas
- Otherwise, perform a comprehensive codebase review
- Look for bugs, design issues, code quality problems, and technical debt
- Don't hallucinate - only report real, observable issues
2. **Categorize findings**:
- **Bugs**: Incorrect behavior, logic errors, potential crashes
- **Design issues**: Architecture problems, poor separation of concerns
- **Code quality**: Readability, maintainability, performance issues
- **Security**: Potential vulnerabilities or unsafe practices
- **Technical debt**: Outdated dependencies, deprecated APIs, code duplication
3. **Document each issue thoroughly**:
- **Title**: Clear, descriptive summary of the problem
- **Description**: Detailed explanation of the issue
- **Code location**: Specific files and line numbers
- **Impact**: How this affects the system or users
- **Reproduction steps**: If applicable, how to reproduce the issue
- **Suggested solution**: Potential approaches to fix the problem
4. **Check for duplicates**:
- Use `gh issue list` to see existing issues
- Search for similar problems already reported
- Only create new issues for genuinely new problems
5. **Create GitHub issues**:
- Use `gh issue create` for each identified problem
- Apply appropriate labels: `bug`, `enhancement`, `security`, `tech-debt`
- Set priority levels based on severity and impact
- Include code snippets and file references in issue descriptions
6. **Generate a summary report**:
- Create `code-review-findings.md` with:
- Overview of issues found
- Categorization by type and priority
- Recommendations for addressing issues
- Links to created GitHub issues
Remember: Be specific and accurate - provide concrete evidence for each issue identified.Related Skills
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