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GitHub can show an issue as open and unassigned even when someone has already volunteered, a pull request is underway, or maintainers have said the project is not ready for contributions. Ankith M says that mismatch left his friend with no available beginner-friendly issues in one search of the Kestra repository, so he built Unclaimed to make the evidence easier to see.
Why an open GitHub issue may not be available
“Open” describes an issue’s GitHub state; it does not guarantee that the work is unclaimed or ready for a new contributor. An issue may still be open while a contributor has asked to take it, a pull request is linked to it, or maintainers have marked it as not ready for contributions. A search limited to open, unassigned issues can therefore return candidates that are already spoken for.
That distinction matters especially to beginners looking for a first contribution. Finding an issue that is both suitable and genuinely available takes more than checking its status and assignee fields.
What Ankith M found in the Kestra search
In an article published on DEV Community on October 4, 2026, Ankith M reported that a GitHub search for open, unassigned good-first-issue results in kestra-io/kestra returned 31 issues. In his review, none appeared free:
#1 Best Overall
| What the author reported | Count |
|---|---|
| Open, unassigned good-first-issue results returned by the search | 31 |
| Already had a pull request | 21 |
| Had people asking to work on them | 8 |
| Marked “not ready for contributions” | 2 |
| He judged actually free | 0 |
These are Ankith M’s figures for that review, not a current GitHub count or a general measure of how often beginner issues are unavailable. He also reported that one issue had three pull requests and another had as many as 11 people asking to work on it. The article does not provide an issue-by-issue appendix or a reproducible query, so the snapshot should be read in that context.
How Unclaimed is described as checking issues
Ankith M describes Unclaimed as checking straightforward GitHub signals first, then using a language model to interpret comments when the structured facts do not settle whether an issue is available.
First, check direct GitHub signals
The tool checks whether an issue has assignees, looks for linked pull requests—including open or merged pull requests visible in the issue timeline—and checks for a project’s “not ready for contributions” banner. These signals can rule out an issue without asking a model to infer intent from conversation.
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Then, interpret comments where needed
For unresolved cases, the article says Unclaimed uses Gemma 3 4B through Ollama to read the issue and recent comments. It returns structured details such as who requested assignment, who says they are working on the issue, maintainer decisions, a short task summary, and a difficulty estimate. Ankith M says the result presents supporting evidence—the relevant comment, the person’s avatar, the date, and a link—so a reader can inspect the basis for a status rather than treating it as an unexplained verdict.
The article describes using the tool with either a public repository or a single issue link. Project conventions differ, however: for example, Ankith M says Unclaimed recognized Zed’s .contrib/good first issue label and found four of the 15 newest issues free. That is his reported demonstration, not an independent test of the current repository or tool.
What the build revealed about automating availability checks
The project was built with plain Node.js, a single HTML page, Gemma through Ollama, and two Railway containers, according to Ankith M. His account describes practical limits that explain why a label or a single API field may not be enough.
- Model size affected results: Ankith M says the 1B model missed claimants in one example, while moving to 4B improved results. He reported CPU processing times of about 6 to 16 seconds per issue; those are his reported timings, not a general performance guarantee.
- Rules helped with a narrow judgment: He says a small code rule to correct the model’s output worked better than asking the model alone to identify only self-claims.
- Long model responses could time out: Requests sometimes expired while the model generated long JSON.
- Context affected interpretation: Maintainer comments could be misread when organisation membership was private.
- Token access affected pull-request visibility: Ankith M says open pull requests appeared absent with a read-only GitHub token. He addressed this by also searching for pull requests that mention the issue.
These are implementation observations from the author’s account, not an independent software audit. They point to an important limitation of any automated status label: comment intent can be ambiguous, and incomplete API visibility can make an issue look less active than it is.
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Ankith M writes, “I checked it against 8 real issues I had read by hand, and it got all 8 right.” That is a small, self-reported check against eight issues, not independent evidence of accuracy across repositories, label systems, or comment styles. The tool’s evidence links are useful for review, but contributors should still open the issue and verify its current status before starting work.
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