Your Repo's Issue Queue Is a Mess. Here's a Free Model Triage Workflow That Actually Runs

Description and use

Automated GitHub issue triage using a free AI model and free server that classifies new issues into four categories and posts a comment suggestion.

Problem

Maintainers struggle with large, unstructured issue queues, spending time manually sorting issues into categories such as bug, feature, question, or duplicate.

Outcome

In a test repo with ~40 open issues, the model correctly labeled 34 issues; the remaining 6 were ambiguous. The workflow runs entirely on free tiers, requiring no GPU or credit card.

Workflow

• 1. Configure a free server (e.g., MonkeyCode) to host the script.
• 2. Set environment variables MONKEYCODE_API_URL and MONKEYCODE_API_KEY.
• 3. Deploy the Python script that receives GitHub webhook payloads.
• 4. Register a GitHub webhook for the repository with payload URL pointing to the server, content type application/json, and event Issues.
• 5. When a new issue opens, GitHub sends payload to the server.
• 6. The handler extracts title and body, truncates body to 2000 chars, and builds a prompt.
• 7. The script calls the free model with temperature 0 and max_tokens 10, requesting one of four labels.
• 8. The model returns a single word (bug, feature, question, duplicate).
• 9. The script posts a comment on the issue with the suggested label.
• 10. Maintainer reviews the comment and applies the label if desired.

Evidence

I tested this workflow against a small repo with about 40 open issues. The model correctly labeled 34 of them.

Source

The goal is small. A webhook receives a new issue. The script pulls the issue title and body. It sends both to a free model and asks for one of four labels: bug, feature, question, or duplicate.

Source