Meet the Workflows: Organization & Cross-Repo

Jan 13, 2026

Don Syme

Peli de Halleux

Mara Kiefer

Let’s zoom out at Peli’s Agent Factory!

In our previous post, we explored multi-phase improver workflows - our most ambitious agents that tackle big projects over multiple days, maintaining state and making incremental progress. These workflows proved that AI agents can handle complex, long-running initiatives when given the right architecture.

But all that sophisticated functionality has focused on a single repository. What happens when you zoom out to organization scale? What insights emerge when you analyze dozens or hundreds of repositories together? What looks perfectly normal in one repo might be a red flag across an organization. Organization and cross-repo workflows operate at enterprise scale, requiring careful permission management, thoughtful rate limiting, and different analytical lenses. Let’s explore workflows that see the forest, not just the trees.

Organization & Cross-Repo Workflows

These agents work at organization scale, across multiple repositories:

Org Health Report - Organization-wide repository health metrics - 4 organization health discussions created
Stale Repo Identifier - Identifies inactive repositories - 2 issues flagging truly stale repos
Ubuntu Image Analyzer - Documents GitHub Actions runner environments - 4 merged PRs out of 8 proposed (50% merge rate)

Scaling agents across an entire organization changes the game. Org Health Report has created 4 organization health discussions analyzing dozens of repositories at scale - for example, #6777 with the December 2025 organization health report. It identifies patterns and outliers (“these three repos have no tests, these five haven’t been updated in months”).

Stale Repo Identifier has created 2 issues flagging truly stale repositories for organizational hygiene - for example, #5384 identifying Skills-Based-Volunteering-Public as truly stale. It helps find abandoned projects that should be archived or transferred.

We learned that cross-repo insights are different - what looks fine in one repository might be an outlier across the organization. These workflows require careful permission management (reading across repos needs organization-level tokens) and thoughtful rate limiting (you can hit API limits fast when analyzing 50+ repos).

Ubuntu Image Analyzer has contributed 4 merged PRs out of 8 proposed (50% merge rate), documenting GitHub Actions runner environments to keep the team informed about available tools and versions. It’s wonderfully meta - it documents the very environment that runs our agents.

Using These Workflows

You can add these workflows to your own repository and remix them. Get going with our Quick Start, then run one of the following:

Org Health Report:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/org-health-report.md

Stale Repo Identifier:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/stale-repo-identifier.md

Ubuntu Image Analyzer:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/ubuntu-image-analyzer.md

Then edit and remix the workflow specifications to meet your needs, regenerate the lock file using gh aw compile, and push to your repository. See our Quick Start for further installation and setup instructions.

You can also create your own workflows.

Learn More

GitHub Agentic Workflows - The technology behind the workflows
Quick Start - How to write and compile workflows

Next Up: Advanced Analytics & ML Workflows

Cross-repo insights reveal patterns, but we wanted to go even deeper - using machine learning to understand agent behavior.

Continue reading: Advanced Analytics & ML Workflows →

This is part 17 of a 19-part series exploring the workflows in Peli’s Agent Factory.