As AI agents grow in capability, more people feel empowered to code and contribute to open source. The ceiling feels higher than ever. That is a net positive for the ecosystem, but it also changes the day-to-day reality for maintainers. Maintainers like the goose team face a growing volume of pull requests and issues, often faster than they can realistically process.
We embraced this reality and put goose to work on its own backlog.
We actually used goose pre-1.0 to help us build goose 1.0. The original goose was a Python CLI, but we needed to move quickly to Rust, Electron, and an MCP-native architecture. goose helped us make that transition. Using it to triage issues and review changes felt like a natural ex…
As AI agents grow in capability, more people feel empowered to code and contribute to open source. The ceiling feels higher than ever. That is a net positive for the ecosystem, but it also changes the day-to-day reality for maintainers. Maintainers like the goose team face a growing volume of pull requests and issues, often faster than they can realistically process.
We embraced this reality and put goose to work on its own backlog.
We actually used goose pre-1.0 to help us build goose 1.0. The original goose was a Python CLI, but we needed to move quickly to Rust, Electron, and an MCP-native architecture. goose helped us make that transition. Using it to triage issues and review changes felt like a natural extension, so we embedded goose directly into a GitHub Action.
Credit: That GitHub Action workflow was built by Tyler Longwell, who took an idea we had been exploring manually and turned it into something any maintainer could trigger with a single comment.
Before the GitHub Action
Before the GitHub Action existed, the goose team was already using goose to accelerate our issue workflow. Here’s a real example.
A user reached out on Discord asking why an Ollama model was throwing an error in chat mode. Rather than digging through the codebase myself, I asked goose to explore the code, identify the root cause, and explain it back to me. Then, I asked goose to use the GitHub CLI to open an issue.
During that same session, goose mentioned it had 95% confidence it knew how to fix the problem. The change was small, so I asked goose to open a PR. It was merged the same day.
This kind of workflow has changed how I operate as a Developer Advocate. Before goose, when a user reported a problem, the process unfolded in fragments. I would ask clarifying questions, check GitHub for related issues, pull the latest code, grep through files, read the logic, and try to form a hypothesis about what was going wrong.
If I figured it out, I had two options:
- I could write up a detailed issue and add it to a developer’s backlog, which meant someone else had to context-switch into the problem later.
- Or I could attempt the fix myself, which often led to more time spent and more back-and-forth during code review if I got something wrong.
Either way, the process stretched across hours or days. And if the problem wasn’t high priority, it sometimes slipped through the cracks. The report would sit in Discord or a GitHub comment until it scrolled out of view, and the user would assume nobody was listening.
With goose, that entire process collapsed into a single conversation.
The local workflow works. But when I solve an issue locally with goose, I’m still the one driving. I stop what I’m doing, open a session, paste the issue context, guide goose through the fix, run the tests, and open the PR.
Scaling with a GitHub Action
The GitHub Action compresses that entire sequence into a single comment. A team member sees an issue, comments /goose, and moves on. goose spins up in a container, reads the issue, explores the codebase, runs verification, and opens a draft PR. The maintainer returns to a proposed solution rather than a blank slate.
We saw this play out with issue #6066. Users reported that goose kept defaulting to 2024 even though the correct datetime was in the context. The issue sat for two days. Then Tyler saw it, commented /goose solve this minimally at 1:59 AM, and went back to whatever he was doing (presumably sleeping). Fourteen minutes later, goose opened PR #6101.
The maintainer’s role shifts from implementing to reviewing. The bottleneck in open source is rarely "can someone write this code." It’s "can someone with enough context find the time to write this code." The GitHub Action decouples those two constraints. Any maintainer can trigger a fix attempt without deep familiarity with that part of the codebase.
This scales in a way manual triage cannot. A backlog contains feature requests, complex bugs, and quick fixes in equal measure. The Action lets you point at an issue and say "try this one" without committing your afternoon. If goose fails, you lose minutes of compute. If it succeeds, you save hours.
For contributors, responsiveness changes everything. When a user filed issue #6232 about slash commands not handling optional parameters, a maintainer quickly commented /goose can you fix this, and within the hour there was a draft PR with the fix and four new tests. Even if the PR is not perfect and needs adjustments, contributors see momentum.
Under the Hood
Maintainers summon goose with /goose followed by a prompt as a comment on an issue. GitHub Actions spins up a container with goose installed, passes in the issue metadata, and lets goose work. If goose produces changes and verification passes, the workflow opens a draft pull request.
But there’s more happening under the hood than a simple prompt like "/goose fix this."
The workflow uses a recipe that defines phases to ensure goose actually accomplishes the job and doesn’t do more than we ask it to.
| Phase | What goose does | Why it matters |
|---|---|---|
| Understand | Read the issue and extract all requirements to a file | Forces the AI to identify what "done" looks like before writing code |
| Research | Explore the codebase with search and analysis tools | Prevents blind edits to unfamiliar code |
| Plan | Decide on an approach | Catches architectural mistakes before implementation |
| Implement | Make minimal changes per the requirements | "Is this in the requirements? If not, don’t add it" |
| Verify | Run tests and linters | Catches obvious failures before a human sees the PR |
| Confirm | Reread the original issue and requirements | Prevents the AI from declaring victory while forgetting half the task |
The recipe also gives goose access to the TODO extension, a built-in tool that acts as external memory. The phases tell goose what to do. The TODO helps goose remember what it’s doing. As goose reads through the codebase and builds a solution, its context window fills up and earlier instructions can be compressed or lost. The TODO persists, so goose can always check what it’s done and what’s left.
The workflow also enforces guardrails around who can invoke /goose, which files it’s allowed to touch, and the requirement that a maintainer review and approve every PR.
There’s something strange about using goose to maintain goose. But it keeps us honest. We’re our own first customer, and if the agent can’t produce mergeable PRs here, we feel it immediately.
The future we’re aiming for isn’t one where AI replaces maintainers. It’s one where a maintainer can point at a problem, say "try this," and come back to a concrete proposal instead of a blank editor.
If that becomes the norm, open source scales differently.
The GitHub Action workflow is public for anyone who wants to explore this pattern in their own CI pipeline.