thelobbi Linear Agents
References:
- Agents (user docs): https://linear.app/docs/agents-in-linear
- Linear Agent: https://linear.app/docs/linear-agent
- Coding sessions: https://linear.app/docs/coding-sessions
- Developer docs: https://linear.app/developers/agents
Corrections to earlier versions of this plugin
Version 1.0.0 of this skill described an API that does not exist. If you find these anywhere, they are wrong:
| Wrong | Right |
|---|---|
| "AIG = Agent Intelligence Gateway", a message bus | AIG = Agent Interaction Guidelines, a set of UX rules. There is no gateway. |
agentSignalCreate({ kind: "progress" }) |
agentActivityCreate with a typed content payload |
Signal kinds progress / completion / request_input |
Activity types thought / action / elicitation / response / error |
Scopes agents:create, agents:signal |
app:assignable, app:mentionable (plus read / write) |
lib/aig.ts, aigPublish() / aigSubscribe() |
Never existed. Use lib/agent-session.mjs. |
| Actor tokens minted by your backend as 5-minute JWTs | OAuth with actor=app; see skills/linear-oauth/SKILL.md |
What an agent is
An agent is an OAuth application installed into a workspace as an app user. Once installed it behaves like a teammate: it can be @mentioned, delegated issues, and can comment on issues, projects and documents.
The key semantic: assigning an issue to an agent is delegation, not reassignment. The human assignee stays the owner and remains accountable. Do not write copy that implies the agent has taken the issue over.
Agents are not billable seats. They cannot sign in, access admin functions, or manage users.
Agent sessions and activities
Work is reported through a session, which holds an ordered list of activities.
Session states: pending, active, error, awaitingInput, complete, stale.
| Activity | Meaning | Terminal |
|---|---|---|
thought |
Internal reasoning, rendered collapsed | no |
action |
A tool or command that was run | no |
elicitation |
A question for the user; moves session to awaitingInput |
no |
response |
Finished successfully | yes |
error |
Failed | yes |
Two timing rules that cause silent breakage when missed:
- Emit within 10 seconds of session creation, or Linear marks it
stale. Send athoughtbefore doing any slow setup work. - A webhook receiver must return 2xx within 5 seconds. Acknowledge first, then work asynchronously.
Use lib/agent-session.mjs, which enforces both:
import { AgentSession } from "../lib/agent-session.mjs";
const session = new AgentSession(client, sessionId);
await session.thought("Reading the issue and planning the change.");
await session.action("Run tests", "pnpm test", "42 passed");
await session.respond("Opened PR #128.");
session.guard(work) wraps a unit of work so the session always terminates —
a crashed worker still emits error rather than leaving the issue stuck in
active forever. This is the single most important guardrail in the module.
Agent guidance
Workspaces and teams publish markdown guidance that is passed to every agent working there: which repository to use, how to reference issues in commits, what review process to follow.
- Workspace: Settings → Agents → Additional guidance
- Team: team settings → Agents → Additional guidance (takes priority)
Read guidance before acting and follow it. It is the workspace's convention layer, and users will judge the agent by whether it respects it.
Agent Skills
Users can save a good agent interaction as a reusable skill, invoked by slash command in the agent input or selected automatically when the context matches.
- Personal: Settings → Account → Agent personalization → Skills
- Team-shared: team settings → AI & Agents → Agent skills
Enumerate them with the Linear MCP tools list_agent_skills / get_agent_skill
before proposing a workflow — the team may already have a sanctioned one.
Loops
Loops are shared skills that run on a schedule or an event, letting Linear do background work: triage delegation, follow-ups, routine sweeps. Prefer a Loop over an external cron when the work is entirely inside Linear; use this plugin's conductor when the work needs a repository checkout.
Coding sessions
Linear can run agentic coding itself. Delegating an issue starts a secure session on Claude Code or Codex in a managed sandbox, which drafts a PR and attaches the diff to the issue.
- Models include Claude Fable 5, Claude Opus 5, Claude Sonnet 5, GPT-5.6 Sol.
- Coding environments (Workspace settings → AI & Agents → Coding sessions → Environments) configure runtimes, env vars, a prepare script, files, and repository-specific guidance. One repository per environment.
- Triage automations can start a coding session on arrival, filtered by label or other properties.
- Usage draws on the workspace's AI credits.
- Requires the GitHub integration with code access.
Choosing between a coding session and this plugin's swarm
| Use Linear coding sessions | Use /linear:swarm |
|---|---|
| One issue at a time, GitHub repo | Many issues in parallel, bounded concurrency |
| No local infrastructure wanted | You need your own runners, caches, or network |
| Managed sandbox is sufficient | Harness Code, or any non-GitHub host |
| Billed via Linear AI credits | Billed via your own Claude Code usage |
They compose: let Linear take first pass on triage, and run the swarm for planned cycle work.
Writing issues an agent can act on
Delegation quality tracks issue quality. A well-scoped issue names the file or subsystem, states the expected behaviour change, points at the existing pattern to reuse, and says explicitly what must not change. A vague issue burns credits on exploration.