Allison highlights two new GitHub Actions for open source maintainers: an AI assessment comment labeler and an AI moderator, both using the GitHub Models inference API to automate triage and moderation.

New GitHub Actions for AI-Based Issue Labeling and Moderation

GitHub has introduced two powerful GitHub Actions to assist open source maintainers with automating issue triage and moderation through the GitHub Models inference API:

AI Assessment Comment Labeler

Purpose: Automate the triage of issues by applying trigger labels and running AI assessments.
How it works:
- Configurable to act on specific labels and issue templates.
- Runs multiple AI prompts in parallel, generating standard labels such as ai:bug-review:ready for review.
- These labels streamline filtering, searching, and automating project workflows.
- Option to suppress comments for seamless integration into existing automation pipelines.

AI Moderator

Purpose: Automatically scan issues and comments for spam, link spam, or AI-generated content.
Key Features:
- Auto-label and optionally minimize flagged content.
- Supports custom moderation prompts for flexible enforcement.

Shared Features

Both actions use your workflow’s GITHUB_TOKEN with models: read permissions, eliminating the need for extra API keys.
Prompts are customizable—maintainers can use multiple prompt files for assessments and tailor moderation prompts.
Designed to integrate with existing workflows for reduced manual workload and improved open source community health.

Additional Resources

Explore the AI inference action reference template for custom automation.
Read the full models documentation.
Join community discussions for questions and feedback.

These actions aim to make open source maintenance more efficient by leveraging AI-powered automation within GitHub workflows.

This post appeared first on “The GitHub Blog”. Read the entire article here