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OpenAI linear

OpenAI linearSkill

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Summary

OpenAI's skill for driving Linear through its MCP server: a read-first workflow for issue triage, sprint planning, documentation audits and workload balancing, with the MCP setup and OAuth steps written out.

Features

  • Writes out the Linear MCP setup: remote server URL, remote MCP client flag, OAuth login and the required restart
  • WSL fallback config for Windows connection errors
  • Read-first execution order: list/get/search to build context before any create or update
  • Explains the grouping logic before applying a bulk change
  • Nine concrete plays, from sprint planning and bug triage to retrospectives and dependency discovery
  • Troubleshooting for OAuth, missing required fields and Linear API rate limits

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Skill Content

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---
name: linear
description: Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.
metadata:
  short-description: Manage Linear issues in Codex
---

# Linear

## Overview

This skill provides a structured workflow for managing issues, projects & team workflows in Linear. It ensures consistent integration with the Linear MCP server, which offers natural-language project management for issues, projects, documentation, and team collaboration.

## Prerequisites
- Linear MCP server must be connected and accessible via OAuth
- Confirm access to the relevant Linear workspace, teams, and projects

## Required Workflow

**Follow these steps in order. Do not skip steps.**

### Step 0: Set up Linear MCP (if not already configured)

If any MCP call fails because Linear MCP is not connected, pause and set it up:

1. Add the Linear MCP:
   - `codex mcp add linear --url https://mcp.linear.app/mcp`
2. Enable remote MCP client:
   - Set `[features] rmcp_client = true` in `config.toml` **or** run `codex --enable rmcp_client`
3. Log in with OAuth:
   - `codex mcp login linear`

After successful login, the user will have to restart codex. You should finish your answer and tell them so when they try again they can continue with Step 1.

**Windows/WSL note:** If you see connection errors on Windows, try configuring the Linear MCP to run via WSL:
```json
{"mcpServers": {"linear": {"command": "wsl", "args": ["npx", "-y", "mcp-remote", "https://mcp.linear.app/sse", "--transport", "sse-only"]}}}
```

### Step 1
Clarify the user's goal and scope (e.g., issue triage, sprint planning, documentation audit, workload balance). Confirm team/project, priority, labels, cycle, and due dates as needed.

### Step 2
Select the appropriate workflow (see Practical Workflows below) and identify the Linear MCP tools you will need. Confirm required identifiers (issue ID, project ID, team key) before calling tools.

### Step 3
Execute Linear MCP tool calls in logical batches:
- Read first (list/get/search) to build context.
- Create or update next (issues, projects, labels, comments) with all required fields.
- For bulk operations, explain the grouping logic before applying changes.

### Step 4
Summarize results, call out remaining gaps or blockers, and propose next actions (additional issues, label changes, assignments, or follow-up comments).

## Available Tools

Issue Management: `list_issues`, `get_issue`, `create_issue`, `update_issue`, `list_my_issues`, `list_issue_statuses`, `list_issue_labels`, `create_issue_label`

Project & Team: `list_projects`, `get_project`, `create_project`, `update_project`, `list_teams`, `get_team`, `list_users`

Documentation & Collaboration: `list_documents`, `get_document`, `search_documentation`, `list_comments`, `create_comment`, `list_cycles`

## Practical Workflows

- Sprint Planning: Review open issues for a target team, pick top items by priority, and create a new cycle (e.g., "Q1 Performance Sprint") with assignments.
- Bug Triage: List critical/high-priority bugs, rank by user impact, and move the top items to "In Progress."
- Documentation Audit: Search documentation (e.g., API auth), then open labeled "documentation" issues for gaps or outdated sections with detailed fixes.
- Team Workload Balance: Group active issues by assignee, flag anyone with high load, and suggest or apply redistributions.
- Release Planning: Create a project (e.g., "v2.0 Release") with milestones (feature freeze, beta, docs, launch) and generate issues with estimates.
- Cross-Project Dependencies: Find all "blocked" issues, identify blockers, and create linked issues if missing.
- Automated Status Updates: Find your issues with stale updates and add status comments based on current state/blockers.
- Smart Labeling: Analyze unlabeled issues, suggest/apply labels, and create missing label categories.
- Sprint Retrospectives: Generate a report for the last completed cycle, note completed vs. pushed work, and open discussion issues for patterns.

## Tips for Maximum Productivity

- Batch operations for related changes; consider smart templates for recurring issue structures.
- Use natural queries when possible ("Show me what John is working on this week").
- Leverage context: reference prior issues in new requests.
- Break large updates into smaller batches to avoid rate limits; cache or reuse filters when listing frequently.

## Troubleshooting

- Authentication: Clear browser cookies, re-run OAuth, verify workspace permissions, ensure API access is enabled.
- Tool Calling Errors: Confirm the model supports multiple tool calls, provide all required fields, and split complex requests.
- Missing Data: Refresh token, verify workspace access, check for archived projects, and confirm correct team selection.
- Performance: Remember Linear API rate limits; batch bulk operations, use specific filters, or cache frequent queries.

Usage Instructions

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Generic Instructions
npx skills add https://github.com/openai/skills --skill linear

Requires the Linear MCP server connected over OAuth at https://mcp.linear.app/mcp.

Example Usage

Triage the open bugs on the Platform team in Linear: rank the critical and high ones by user impact, and move the top five to In Progress.

Description

Linear's MCP server gives an agent the tools; it does not tell the agent how to use them well. linear is OpenAI's curated skill that supplies the missing procedure — and, usefully, the setup steps people get stuck on.

It starts before the first tool call

Step 0 is the Linear MCP connection itself: add the remote server (https://mcp.linear.app/mcp), enable the remote MCP client, complete the OAuth login, and — the detail that saves an afternoon — restart the client afterwards, which the skill tells the user explicitly rather than looping on a failing call. There is a documented WSL fallback for Windows connection errors, routing through mcp-remote over SSE.

The workflow

After setup it enforces four ordered steps: clarify the goal and scope (team, project, priority, labels, cycle, due dates); choose the workflow and confirm the identifiers the tools will need; execute in logical batches — read first with list/get/search to build context, then create and update, explaining the grouping before any bulk change; and finally summarise results, remaining gaps and proposed next actions.

The read-before-write rule and the explain-before-bulk rule are the two that matter. Bulk operations on an issue tracker are hard to undo and easy to get subtly wrong, and narrating the grouping first gives you a chance to stop it.

What it covers

The skill maps the Linear MCP tool surface — issues (list_issues, get_issue, create_issue, update_issue, list_my_issues, statuses, labels), projects and teams (list_projects, create_project, update_project, list_teams, list_users), and documentation and collaboration (list_documents, search_documentation, list_comments, create_comment, list_cycles) — onto nine concrete plays: sprint planning, bug triage, documentation audit, workload balancing, release planning, cross-project dependency discovery, automated status updates, smart labelling and sprint retrospectives.

It closes with a troubleshooting section for the three things that actually go wrong: OAuth and workspace permissions, tool-calling errors from missing required fields, and Linear's API rate limits when a bulk operation runs too wide.

Who it is for

Engineering teams running Linear who want an agent to do the tracker housekeeping — triage, labelling, retro reports — without it quietly reorganising a board. From OpenAI's curated public skills collection.

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