Choose a first issue you can understand, complete as one focused change, and verify—not just one carrying a good first issue label. Start with an AI project you use or want to learn, check that its contribution process is active, then confirm the issue is clear, still wanted, and testable before you begin.
What makes an issue a good first contribution?
A strong first issue has a concrete expected result, a manageable scope, and a way to check whether the change works. It should also fit your current skills or offer a small, realistic stretch. A task that looks beginner-friendly but requires unfamiliar infrastructure, broad design decisions, or access you do not have may be a poor first choice.
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The change does not have to touch model code. Depending on the project’s rules, useful first contributions can include improving documentation or setup instructions, adding or fixing tests, reproducing a reported bug, or testing a pull request. Pick work that helps the project and can be reviewed as a focused contribution.
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Start with software you already use, care about, or want to understand. Familiarity with its purpose makes issue context easier to follow; a language and toolchain you know make it easier to build, test, and explain a change. The Open Source Guides recommends starting with projects you already use or want to use.
Before committing to an issue, check whether you can follow the project’s setup instructions and work with its language, package manager, and required environment. A small documentation or test task in a familiar repository may be a better first contribution than an ambitious change to a model architecture.
Check the project’s rules and contribution activity
Read the README and contribution guide, often named CONTRIBUTING.md. Check the project’s license, supported versions, coding conventions, test commands, and policies for contributions, including any rules on AI assistance. Follow the repository’s instructions when they differ from general advice. GitHub’s contribution guide also points contributors to project materials such as the code of conduct, license, and security policy.
Then look for evidence of a functioning contribution and review loop:
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- Recent commits to the main branch or equivalent.
- Recent issue activity, including whether issues receive replies or are closed with an explanation.
- Pull requests that receive constructive review and are sometimes merged.
- Maintainer responses that give contributors a clear next step.
These are signals, not guarantees. A busy repository can still be slow to review your change, and no single activity measure predicts whether a specific pull request will be accepted.
Find candidates, but treat labels as clues
On GitHub, search a repository’s issues for good first issue or help wanted. You can also check the repository’s /contribute page, which may surface opportunities. GitHub describes the good first issue label as a way to highlight opportunities and make them easier to discover. It is a discovery aid, not proof that an issue is easy, current, unclaimed, or clearly specified.
Open the issue and read its description and comments, along with any linked documentation or project guidance. Assess it against these questions:
- Outcome: Is it clear what should change or what question needs answering?
- Scope: Can you make and review the work as one focused change, without settling larger design questions?
- Verification: Can you reproduce the bug, run a relevant test, or otherwise demonstrate that the task is complete?
- Starting point: Does the issue identify relevant files, packages, examples, or an investigation step?
- Status: Are the comments current, and is someone already assigned or actively working on it?
- Fit: Do you have the required tools and knowledge, or can you learn the missing part within the issue’s limited scope?
An issue that fails one of these checks is not automatically unsuitable, but it is a reason to ask questions before investing substantial effort.
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Compare two candidate issues
When several issues look promising, weigh the practical trade-offs rather than choosing the one with the most encouraging label.
| Check | Stronger first-issue signal | Reason to pause |
|---|---|---|
| Skill and tools | Uses a language, environment, or workflow you can already handle. | Depends on unfamiliar infrastructure or access you cannot obtain. |
| Expected result | Describes a specific fix, addition, or investigation outcome. | Uses broad goals such as “improve the model” without a defined result. |
| Scope | Can be completed and reviewed as a focused change. | Requires architecture decisions or mixes several unrelated tasks. |
| Project activity | Recent issues and pull requests show constructive maintainer review. | There is little recent activity or no visible review of outside contributions. |
| Verification | A reproduction, test, expected output, or other completion check is available. | It is unclear how anyone will know whether the work succeeded. |
| Current need | Recent comments or maintainer confirmation indicate the task is still wanted. | The issue is old, assigned, duplicated, or has no clear current status. |
The Good First Issue guidance likewise treats a label as a starting point and emphasizes checking an issue’s clarity, activity, fit, and verifiability.
Ask before coding when status or scope is unclear
If an issue is stale, vague, unlabelled, potentially duplicated, or large enough to involve design choices, comment on it before opening a pull request. State the small scope you propose and ask whether the issue is still active and whether that approach fits the maintainers’ priorities. For substantial feature work, discuss the approach before spending significant time on implementation.
GitHub advises contributors to ask maintainers about an unlabelled issue before opening a pull request; see its open-source contribution guidance. Keep routine questions in the project’s public issue or discussion channels so others can benefit from the answer. If the project does not respond, consider another issue rather than assuming the task is available.
Make the contribution easy to review
- Follow the project’s process. Use its documented fork or branch workflow, development setup, formatting rules, tests, and pull request template.
- Keep the change focused. Address the agreed issue rather than bundling unrelated cleanup or extra features.
- Explain your verification. In the pull request, state what you changed and which relevant checks you ran. If a check could not be run, say so plainly.
- Respond to review. Read feedback, ask focused questions when needed, and update the work in line with the project’s conventions.
A clear, testable pull request gives maintainers a better basis for review, but acceptance is their decision and is not guaranteed.
Use AI tools without handing off responsibility
An AI tool can help explain unfamiliar issue context, suggest where to look, or clarify code. Its output still needs to be checked against the repository and the task. The Open Source Guides advises contributors to verify that AI-generated output is accurate, follows project conventions, and addresses the intended issue; it also says contributors remain responsible for what they submit.
Understand every part of your diff, run the relevant checks, and follow any project-specific AI policy. Do not submit code or explanations you cannot evaluate and stand behind.
Recheck availability before starting
Issue labels, assignments, comments, maintenance activity, and contribution policies can change. Reopen the issue and check its latest comments and status immediately before beginning; a label you saw earlier does not reserve the work or establish that maintainers still want it.
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