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laya-triage is in Hacktoberfest: free, local AI triage for GitHub issues

A developer released laya-triage, a GitHub Action that automatically labels new issues as bug, feature request, question, or documentation problem using a fine-tuned open-weight model running locally inside the user's own GitHub Actions runner. The tool requires no API key, incurs no cost, and keeps issue text within GitHub, and it ships in dry-run mode by default so maintainers can preview its decisions before enabling live labeling. The project is participating in Hacktoberfest and is soliciting mislabel reports as training data for its next model.

by read1 min views4 publishedOct 1, 2026

Every popular repository has the same problem: new issues pile up faster than maintainers can read them, and many end up mislabeled or go unaddressed.

laya-triage is a GitHub Action that reads each new issue and labels it as a bug, feature request, question, or documentation problem. If a bug report is almost empty, it asks the author for the missing details. It runs a fine-tuned open-weight model inside your own GitHub Actions runner: no API key, no cost, and the issue text never leaves GitHub.

🔗 Repo: https://github.com/elnachto/laya-triage

🛒 Marketplace: https://github.com/marketplace/actions/laya-triage

🤗 Models: https://huggingface.co/elnachto/laya-triage-en

Create .github/workflows/triage.yml:

name: Triage

on:
  issues:
    types: [opened]

permissions:
  contents: read
  issues: write

jobs:
  triage:
    runs-on: ubuntu-latest
    steps:
      - uses: elnachto/laya-triage@v1

It starts in dry-run mode, so it prints decisions only in the workflow log. When you like what you see, set dry-run: "false". There are more ready-to-copy workflows in docs/examples/.

type: bug, kind/feature…) and learns how common each issue type is from the issues you labeled in the last year. Some good first issue tasks are still open, and none of them needs a GPU:

The most valuable contribution of all: install it in your repo and tell me when it gets a label wrong (there’s an issue form for that). Those reports are the training data for the next model.

Please read the contributing guide and leave a comment on an issue prior to beginning. A big thank you to the first contributors who have already submitted pull requests! Happy Hacktoberfest 🎃

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