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Worth My Time is a beginner-focused tool that helps GitHub newcomers decide whether an open “good first issue” is worth investigating. It checks for signs that the issue may already be claimed, covered by an open pull request, or attached to a repository with limited activity or newcomer merge history. Its Go, Risky, or Skip verdict is based on explicit heuristics; an optional local AI feature writes a supporting brief rather than deciding the verdict.
Why a “good first issue” label is not enough
The tool grew out of the author’s account of a friend finding eight issues in a row that were already taken. That is one person’s motivating experience, not evidence that most labeled GitHub issues are unavailable.
A label can make an issue easier to find, but it does not answer the practical questions a newcomer needs settled before investing time: “can I work on this?” and “is there already an open PR for it?” Worth My Time is designed to surface clues that those questions deserve a closer look.
What the tool checks
According to the project’s author, users can enter a GitHub username or programming languages. The tool searches for open issues labeled “good first issue,” then evaluates up to 15 candidates using several repository and issue signals.
#1 Best Overall
- Claim indicators: comments that suggest another contributor has taken the issue.
- Competing work: whether an open pull request may already address it.
- Repository context: activity and history of merging contributions from newcomers.
- Issue readiness: how much detail the issue provides and whether the repository has a CONTRIBUTING guide.
It combines those signals into a Go, Risky, or Skip verdict and shows reasons for the result. The categories are triage guidance, not a guarantee that an issue is available or suitable.
How the optional AI brief fits in
The scoring is described as deterministic Python logic. The AI feature is separate: when a local Ollama model is available, the application can produce a beginner-oriented brief for a selected issue. The project uses Gemma for this task, alongside Python, Gradio, the GitHub REST API, and pytest.
For the brief, the application ranks repository file paths against the issue text and gives the model the ten highest-ranked paths. The author says the model is instructed to choose only from those paths or say it is unsure. It also summarizes the issue and turns the repository’s contribution guide into a checklist.
This design separates the verdict from the generated explanation: repository signals feed the score, while the model helps a newcomer understand likely relevant files and contribution steps. That is the author’s description of the implementation, not an independent reliability evaluation.
What a favorable verdict can—and cannot—tell you
Even a Go result is a reason to investigate, not permission from a maintainer. Comments can be ambiguous or overlooked, an issue can change after it is checked, and repository activity cannot reveal what maintainers will decide. As the author puts it: “These are heuristics, not predictions. They catch the common ways time gets wasted, but they can’t read a maintainer’s mind.”
The author says they checked issues returned by the tool and found the verdicts correct, but gives no sample size, method, or independent evaluation. That report should not be read as an accuracy rate or proof that the tool catches every claimed issue.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you try Worth My Time?
The author did not include a live app link in the project article and says the AI component runs locally. The project was demonstrated rather than released as a partial hosted version, so readers should not assume the tool is currently available to use.
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