# Linear

By pulling in issue information like titles, descriptions, issue states and labels, and stakeholder comments, Baz ensures that every change request is grounded in intent. Reviewers no longer need to flip between Linear and GitHub to understand why a change exists - they get the full story, right where the code is.

Baz uses this context to:

* **Enrich AI-generated summaries and topics** with your business objectives.
* **Tailor prompts** to reflect the goals and constraints from the Linear issue.
* **Help reviewers focus** on what’s important by aligning code changes to the original intent.

<figure><img src="/files/4qp0GkXIH5661akbXcW8" alt=""><figcaption><p>Configure Linear</p></figcaption></figure>

### Configuring Baz & Linear

1. In Baz, open **Settings** → **Integrations** → **Linear**.
2. Click **Connect** and follow the OAuth flow.
3. Once authorized, Baz will automatically attach issue context to any linked change request.

> **Tip:** Baz detects linked issues by issue IDs mentioned in the PR description (e.g. `AB-123`).

### How Baz Uses Linear Issue Data

Once enabled, Baz pulls these fields from linked Linear issues:

* **Title & Description** – to inform review summaries and reasoning.
* **Issue State & Labels** – to set expectations (e.g. bug vs. feature vs. chore).
* **Stakeholder Comments** – injected into review prompts as context.

This context powers workflows that:

1. **Generate summaries** that reflect your Linear objectives.
2. **Break down topics** based on feature or flow relevance.
3. **Guide reviewers** to evaluate changes through the lens of what the issue was designed to accomplish.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.baz.co/basics/integrations/linear.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.