Authored by Kevin Lewis, this article explores practical ways to automate your GitHub Actions workflows using GitHub Models. It guides readers through permissions and shows AI-powered automation for triaging issues, summarizing pull requests, and more.

Automate Your Project with GitHub Models in Actions

Author: Kevin Lewis

Original post on The GitHub Blog

Overview

GitHub Models brings AI directly into GitHub Actions workflows, providing automation for tasks like triage, summarization, and more. This article offers a step-by-step guide on integrating GitHub Models within GitHub Actions, with three detailed workflow examples ranging from simple to advanced automation.

Getting Started: Permissions for GitHub Models

Before introducing AI to your workflows, ensure your workflows have the correct permissions; otherwise, AI calls will fail. Update your permissions block as follows:

permissions:
  contents: read
  issues: write
  models: read

These permissions allow workflows to:

  • Read repository content
  • Read, create, or update issues and comments
  • Access GitHub Models (AI features)

Example 1: Automating Bug Report Triage with AI Inference Action

Objective: Use AI to automatically assess new bug reports for sufficient information and respond if any is missing.

Workflow Setup:

  1. Create .github/workflows/bug-reproduction-instructions.yml in your repository. This workflow triggers on new issues.
name: Bug Report Reproduction Check
on:
  issues:
    types: [opened]
permissions:
  contents: read
  issues: write
  models: read
jobs:
  reproduction-steps-check:
    runs-on: ubuntu-latest
    steps:
      - name: Fetch Issue
        id: issue
        uses: actions/github-script@v7
        with:
          script: |
            const issue = await github.rest.issues.get({
              owner: context.repo.owner,
              repo: context.repo.repo,
              issue_number: context.issue.number
            })
            core.setOutput('title', issue.data.title)
            core.setOutput('body', issue.data.body)
  1. Analyze issue reproducibility with the AI inference action (triggered if the issue is labeled ‘bug’).
- name: Analyze Issue For Reproduction
  if: contains(join(github.event.issue.labels.*.name, ','), 'bug')
  id: analyze-issue
  uses: actions/ai-inference@v1
  with:
    model: mistral-ai/ministral-3b
    system-prompt: |
      Given a bug report title and text for an application, return 'pass' if there is enough information to reliably reproduce the issue...
    prompt: |
      Title: $
      Body: $
  1. If the AI response is not ‘pass’, post a comment asking for missing details.
- name: Comment On Issue
  if: contains(join(github.event.issue.labels.*.name, ','), 'bug') && steps.analyze-issue.outputs.response != 'pass'
  uses: actions/github-script@v7
  env:
    AI_RESPONSE: steps.analyze-issue.outputs.response
  with:
    script: |
      await github.rest.issues.createComment({
        owner: context.repo.owner,
        repo: context.repo.repo,
        issue_number: context.issue.number,
        body: process.env.AI_RESPONSE
      })

Result: Issues with insufficient reproduction details are automatically flagged, requesting users to provide the missing information.

Example: AI bot requests more bug report information on GitHub

Example 2: Generating Release Notes from Merged Pull Requests

Objective: Automatically generate and append release notes from merged PRs using the GitHub CLI and gh-models extension.

Workflow Setup

  1. Create a ‘release’ label and an issue named ‘Publish next release changelog’ with this label.
  2. Add .github/workflows/release-notes.yml to your repository, which triggers when a pull request is closed.
name: Add to Changelog
on:
  pull_request:
    types: [closed]
permissions:
  pull-requests: read
  issues: write
  contents: read
  models: read
jobs:
  add_to_changelog:
    if: github.event.pull_request.merged == true
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
  1. Install gh-models CLI extension:
- name: Install gh-models extension
  run: gh extension install https://github.com/github/gh-models
  env:
    GH_TOKEN: $
  1. Summarize the PR using an AI model and append summary to release issue:
PR_NUMBER="$"

# Fetch PR data

gh pr view "$PR_NUMBER" --json title,body,comments,reviews > pr.json

# AI-generated summary

cat pr.json | gh models run xai/grok-3-mini "...prompt..." > summary.md

# Get release issue

RELEASE_ISSUE=$(gh issue list --label release --limit 1 --json number --jq '.[0].number')

# Fetch and update release issue body

RELEASE_ISSUE_BODY=$(gh issue view "$RELEASE_ISSUE" --json body --jq '.body')
FORMATTED_LINE="- $(cat summary.md) (#$PR_NUMBER)"
NEW_BODY="${RELEASE_ISSUE_BODY}\n${FORMATTED_LINE}"

# Update issue

gh issue edit "$RELEASE_ISSUE" --body "$NEW_BODY"

Example: Release notes auto-generated from merged PRs

Example 3: Summarizing and Prioritizing Issues Weekly

Objective: Schedule a workflow to summarize, categorize, and prioritize issues from the past week.

Workflow Setup

  1. Add .github/workflows/weekly-issue-summary.yml, scheduled for every Monday at 9 a.m.
name: Weekly Issue Summary
on:
  workflow_dispatch:
  schedule:
    - cron: '0 9 * * 1'
permissions:
  issues: write
  contents: read
  models: read
jobs:
  create_weekly_summary:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Install gh-models extension
        run: gh extension install https://github.com/github/gh-models
        env:
          GH_TOKEN: $
  1. Gather open issues from the past week and summarize them with a prompt file:
LAST_WEEK=$(date -d "7 days ago" +"%Y-%m-%d")
gh search issues "created:>$LAST_WEEK" --state=open --json title,body,url --repo $ > issues.json
cat issues.json | gh models run --file prompts/issue-summary.prompt.yml > summary.md

The prompt file (prompts/issue-summary.prompt.yml):

name: Issue summarizer
description: Summarizes weekly issues
model: openai/gpt-4.1
messages:
  - role: system
    content: You are a helpful issue summarizer. When given issue content, respond in markdown format.
  - role: user
    content: "Please summarize the following issues into a few short bullet points. Include links if provided. If possible, pull out general themes and help the team prioritize based on impact. Issues begin here:\n "
  1. Create a new issue with the AI-generated summary:
ISSUE_TITLE="Issue Summary - $(date -d '7 days ago' '+%B %d') to $(date '+%B %d')"
gh issue create --title "$ISSUE_TITLE" --label summary --body-file summary.md

Example: Weekly issue summary issue created with AI-categorized and prioritized issues

Conclusion

GitHub Models and GitHub Actions enable a rich set of possibilities for AI-driven workflow automation. By setting the right permissions and following the illustrated examples, teams can:

  • Automate bug triage
  • Generate and append release notes
  • Summarize and prioritize issues

Explore the GitHub Models catalog and try integrating AI-powered features in your next workflow.

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