What Kinds of Rules and Workflows Can You Create with Maige?

Maige lets you write plain-language rules that tell an AI agent how to handle issues and pull requests in your GitHub repository. You describe the outcome you want — for example, "assign UI-related issues to @username" — and Maige carries it out using GitHub API actions such as labeling, assigning, commenting, reviewing code, and running simple code snippets. The main condition to know up front: Maige has been sunset and new signups are closed, so these workflows are only usable today by existing users or by running the open-source code yourself.

How rules are written

Rules are free-text instructions, not configuration files or a fixed menu of triggers. The site's own examples show the pattern:

• maige always assign UI-related issues to @username
• maige label .env PRs as 'needs-approval' unless opened by @maintainer
• maige review all incoming PRs per our CONTRIBUTING.md

Each rule pairs a condition (what kind of issue or PR) with an action (assign, label, comment, review). Because the instructions are natural language, you can reference your own conventions — a contributor guide, a list of maintainers, a file pattern — rather than mapping everything to predefined settings.

What the agent can actually do

Maige describes itself as "an AI with access to GitHub" that "can do anything you could do in the UI." In practice, the documented capabilities are:

The sandbox is the piece that extends rules beyond GitHub metadata. A rule that needs to check or transform code can run a snippet rather than only commenting or labeling.

Workflow patterns worth building

The examples on the site point to a few reusable patterns:

Triage by area

Route issues to the right owner based on topic — UI issues to a frontend maintainer, for instance. This keeps assignment consistent without a human reading every new issue.

Guardrails on sensitive changes

Flag PRs that touch sensitive files (the site's example is .env) with a needs-approval label, with an exception for trusted maintainers. The rule encodes both the condition and the exemption in one sentence.

Review against your own standards

Point a review rule at your CONTRIBUTING.md so incoming PRs are checked against documented conventions rather than ad hoc preferences.

Community onboarding

Once rules are running, the site suggests telling your community how to use the workflows and monitoring runs in the dashboard, giving feedback where the agent gets it wrong.

What you need to set up first

Connecting a repository creates three things, according to the site: a webhook, embeddings of your entire codebase, and a sandbox environment. The embeddings are what let rules refer to your code meaningfully; the webhook is what triggers runs on issue and PR events. Plan for the initial indexing step before expecting rules to fire.

Practical limits to weigh

• Availability: Maige is a public archive on GitHub. New signups are closed; existing users can still sign in. If you are not an existing user, your path is the open-source code, not the hosted product.
• Pricing, if you are an existing user: the Standard Plan was listed at $30.00 USD/month with the first 30 issues free, billed by usage after that. An Enterprise tier was marked "coming soon."
• Rule quality depends on wording: since rules are natural language, vague instructions produce vague behavior. The site's examples are specific about who, what, and exceptions — mirror that style.
• Verification: the dashboard is where you monitor runs and correct mistakes, so treat early rules as something to watch rather than fire-and-forget.

For a team evaluating this kind of tooling today, the useful takeaway is the rule model itself: plain-language policies over issues and PRs, backed by an agent with GitHub write access and a code sandbox. That pattern is worth replicating whether or not you run Maige specifically.