Should every Linear issue become a PR?

No. A clear implementation issue may become a PR. A vague, stale, or risky issue may produce a triage note, reproduction, estimate, or request for clarification instead.

Can Replicas work from Linear context?

Yes. Replicas can use Linear as a trigger and context source for delegated work, then return an inspectable workspace session and reviewable output.

What makes this different from a simple Linear automation?

A simple automation moves data. A coding agent can inspect the repo, change files, run commands, and explain the result.

Resource

Linear issue to PR

Linear issues are often already written like engineering tasks. Replicas helps teams turn clear issues into cloud coding agent sessions that produce PRs, test runs, notes, or a precise handoff.

Start a workspace Linear docs

Definition

What is Linear issue to PR?

Linear issue to PR is a workflow where a selected issue becomes a delegated software engineering task for an AI coding agent. The agent reads the issue, inspects the repository, makes changes when appropriate, runs checks, and returns a reviewable output.

The output does not have to be a pull request every time. Some issues should produce a reproduction, an investigation note, a test report, a scope estimate, or a comment explaining why the task needs clarification.

Workflow

How Replicas handles Linear work

The strongest Linear workflows are explicit enough for an agent to act but still reviewed by humans before anything merges.

Issue is selected: A teammate links, assigns, labels, or requests an agent on a Linear issue with enough context to start.
Agent prepares a workspace: Replicas opens a cloud workspace with the right repository, environment, files, secrets, setup hooks, and coding harness.
Work is attempted: The agent reads relevant files, edits code when appropriate, runs commands, and records what it tried.
Output is returned: The result can be a PR, failed-test explanation, investigation summary, reproduction steps, or a request for clearer requirements.

Good issues

What kinds of Linear issues work best?

Good agent issues are scoped, concrete, and verifiable. They do not need to be tiny, but the agent should know what success looks like.

Small bugs with reproduction context or failing tests.
UI copy, empty states, loading states, and polish tasks.
Refactors with clear boundaries and commands to run.
Follow-up tasks from code review or CI failures.
Backlog items that need validation before they become real implementation work.

Evaluation

How to evaluate a Linear issue to PR agent

Do not only count PRs. A good Linear agent should protect the backlog from low-quality output and give reviewers enough evidence to decide what happens next.

Does it preserve the Linear issue context in the final handoff?
Does it explain when the issue is under-specified instead of inventing requirements?
Does it run the relevant checks and include the output?
Does it update humans with a concise final note?
Can it work with repo-specific environments, MCPs, setup hooks, and secrets?

FAQ