Writing a good first issue description is one of the most impactful things you can do for an open source project. It lowers the barrier for new contributors, increases the likelihood of someone actually working on the issue, and helps maintainers avoid repeated questions. AI tools have become remarkably capable at helping craft these descriptions, and choosing the right one can save time while improving quality.

What Makes a Good First Issue Description

Before evaluating AI tools, it helps to understand what separates an useful first issue from one that scares away potential contributors. A strong first issue includes:

• Clear context: Why does this issue matter? What problem does it solve?

• Acceptance criteria: What does “done” look like? How will you verify the fix?

• Difficulty level: Is this appropriate for someone new to the project?

• Links to relevant code: Pointing to files or functions that need changes

• Steps to reproduce (for bugs): Exact steps so others can verify the problem

The best AI tools understand these elements and can generate descriptions that hit all these points while matching your project’s tone and conventions.

Claude Code: Best Overall for Issue Writing

Claude Code stands out as the top recommendation for writing first issue descriptions. Its strong reasoning capabilities allow it to understand project context and generate appropriately scoped descriptions.

Why Claude Code Works Well

Claude Code excels at understanding the nuanced requirements of good first issues. It can analyze your existing issues to match your project’s style, suggest appropriate difficulty labels, and provide context that helps newcomers understand what they’re getting into.

Using Claude Code to generate an issue description:

# Initialize Claude Code in your project
claude init

# Ask for help writing a good first issue
claude "Write a good first issue for adding a dark mode toggle to our React component library. The toggle should be accessible and follow our existing theme patterns. Include acceptance criteria and suggested files to modify."

Claude Code’s strength lies in its ability to maintain context across a conversation. You can iteratively refine the issue description by asking follow-up questions like “make this more beginner-friendly” or “add more specific technical details about the implementation.”

ChatGPT: Strong for Quick Drafts

ChatGPT remains a solid choice for quickly generating issue descriptions. The GPT-4 model understands developer workflows and can produce reasonable first issue descriptions with minimal prompting.

Advantages of ChatGPT

The main advantage is speed and accessibility. You can paste in a brief description of what needs to be done, and ChatGPT expands it into a full issue. It works well for straightforward feature requests and bug reports.

Prompt example:

Our project needs a first-time contributor issue for fixing a typo in the documentation. The typo is in the README.md file in the root directory. Write a friendly, welcoming issue description that helps someone new to the project complete this task.

The limitation is that ChatGPT sometimes generates generic descriptions that lack project-specific context. You’ll often need to edit the output to add relevant links, coding conventions, or project-specific details.

Cursor: Good for IDE Integration

Cursor offers an unique approach by integrating AI assistance directly into your development environment. For issue writing, this means you can reference your actual codebase while generating descriptions.

Why Consider Cursor

If you’re already using Cursor for coding, having it help with issue descriptions keeps everything in one workflow. You can highlight relevant code and ask Cursor to generate an issue based on what you’re looking at.

Workflow example:

1. Open the file that needs changes

2. Highlight the relevant code section

3. Use Cursor’s chat to ask: “Write a good first issue for improving this function’s error handling”

4. Copy the generated description to GitHub

Cursor works best when you have specific code to reference. For vaguer feature requests, you might get less useful output.

Gemini: Worth Considering for Google Ecosystem Users

Google’s Gemini has improved significantly and offers a viable alternative, especially if you’re already working within the Google/Cloud ecosystem. It understands infrastructure and DevOps contexts well.

When Gemini Shines

If your first issue relates to cloud infrastructure, CI/CD pipelines, or Google Cloud Platform integrations, Gemini can provide contextually relevant descriptions that reference appropriate documentation and tools.

Example prompt for infrastructure issues:

Write a first issue for our team to set up GitHub Actions caching to speed up our CI pipeline. Our project uses Node.js and currently takes 10 minutes per build.

Tips for Getting Better Results

Regardless of which tool you choose, a few practices improve the quality of generated issue descriptions:

Provide context upfront: The more background you give the AI, the better the output. Include information about your project’s tech stack, contributor experience level, and any existing conventions.

Iterate rather than accept the first draft: AI-generated descriptions are starting points. Edit for clarity, add project-specific links, and ensure the tone matches your community.

Create templates for common issue types: Use AI to help build templates for bug reports, feature requests, and documentation improvements. This consistency helps contributors know what to expect.

Example template generation prompt:

Create a template for first-time contributor issues in our Python Django project. Include sections for: problem description, expected behavior, actual behavior, environment details, and suggested first steps for someone new to Django.

Real-World Template Generation

AI tools excel at creating reusable templates for different issue types. Here’s how to generate them:

# Using Claude Code CLI
claude "Create a GitHub issue template for 'good first issues' in our Python FastAPI project. Include: context, difficulty level, suggested approach, acceptance criteria, and helpful resources. Make it welcoming to new contributors."

Generated template output:

## About This Issue

This issue is marked as a good first issue because it touches a focused part of the codebase and doesn't require deep architectural knowledge of the entire project.

## Context
[AI provides specific context about why this matters]

## Difficulty Level
- Time estimate: 30-60 minutes
- Skills needed: Python basics, familiar with FastAPI
- Learning opportunity: [specific area contributors will learn]

## What You'll Change
Files to modify:
- `src/api/routes.py`
- `tests/test_routes.py`

## Acceptance Criteria
- [ ] Feature implemented as described
- [ ] Tests pass locally
- [ ] Code follows project style guide
- [ ] Documentation updated if needed

## Getting Started
1. [Step by step]
2. [Setup instructions]
3. [Testing locally]

## Questions?
Ask in the issue comments or reach out to @maintainer

Pricing and Tool Comparison

Practical Workflow: Issue Description in 5 Minutes

1. Identify problem (1 min): Understand what needs fixing
2. Gather context (1 min): Find related files or issues
3. Generate draft (1 min): Paste context to AI tool with specific prompt
4. Review and edit (1 min): Ensure tone, accuracy, and completeness
5. Post and monitor (1 min): Add to GitHub, respond to clarifying comments

Most of the time is step 4—editing. Fresh AI output often needs adjustments for project-specific details, links, and tone.

Community Feedback and Common Pitfalls

When generating first issues with AI, avoid these common mistakes:

Too vague: “Fix the bugs in the authentication system.” → Better: “Add email validation error message when signup form receives malformed email addresses.”

Too complex: Avoid describing multiple loosely-related improvements in one first issue. Break them into separate tickets.

Missing context: Don’t assume contributors know your codebase. Link to relevant code files, explain the architecture, mention related documentation.

No difficulty indication: Always be explicit about whether this is truly a “first issue” or if it requires some project knowledge.

Weak acceptance criteria: “Implement the feature” is too vague. “Function handles null input without crashing AND returns validation error message” is specific and testable.

Good first issues attract contributors. Bad ones waste everyone’s time. AI tools help you avoid these pitfalls if you review generated content carefully.

Comparing the Options

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