Linear

by openai49f948faa925No licenseListed Oct 8, 2026Updated Oct 8, 2026

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

FeaturedInstructions onlyProductivity & Workflow
AI-generated overview

Guides an agent through managing Linear issues, projects, cycles and team workflows via the Linear MCP server.

What it does
This skill provides a step-by-step workflow for reading, creating and updating issues, projects, labels, comments, documents and cycles in Linear through the Linear MCP server. It covers setup of the MCP connection, clarification of scope, batched tool calls, and a closing summary with next actions. It also lists practical workflows such as sprint planning, bug triage, documentation audits, workload balancing and release planning.
When to use it
Use it when a user wants to read, create or update tickets, projects or team workflows in Linear. It fits tasks like triaging bugs, planning sprints or cycles, auditing documentation, balancing team workload and planning releases.
Requirements
Requires a connected Linear MCP server with OAuth access to the relevant workspace, teams and projects. Setup instructions reference the codex CLI, config.toml feature flags and remote MCP client options. No scripts are shipped; the skill is instructions only.

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.

Source and attribution

Source:openai/skillsinskills/.curated/linearat commit49f948f

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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