Skill UI

Automate Linear project, issue, cycle, label, and comment workflows via the Composio Linear toolkit exposed through Rube MCP, including prerequisite connection checks and optional custom GraphQL queries for advanced cases.

This skill provides comprehensive management of the entire issue lifecycle within the Linear project management tool. Users can create new issues, update key metadata (priority, labels, due dates), and automate state transitions through defined workflows. It also supports building parent/sub-issue hierarchies, managing issue relations (blocks, duplicates), and adding comments, making it ideal for automating project tracking and workflow enforcement.

Automate core Agile workflows within Linear. This skill allows users to manage the entire project lifecycle, including creating and updating projects, setting milestones, assigning issues, and managing time-boxed cycles (sprints). It also includes advanced functionalities like calculating cycle metrics and rolling over unfinished tasks, significantly streamlining product development and planning processes.

Automates the setup and best practices for linear issue generation within enterprise workflows, offering guidance, production-ready code, and validation for governance, compliance, and integration tasks.

Provides a deep dive into migrating issue tracking data from major platforms like Jira, Asana, and GitHub to Linear. This guide covers the entire lifecycle, including initial workflow assessment, status and priority mapping, data export utilizing API examples, and detailed transformation logic required to ensure data integrity and smooth adoption within Linear.

This skill provides a comprehensive, structured workflow for managing projects, issues, and team workflows within the Linear platform. It supports natural-language project management, allowing users to perform tasks like sprint planning, bug triage, and workload balancing by interacting with the Linear MCP server and various specialized tools.

This skill performs mean-variance portfolio optimization by solving the linear system Σ · x = μ using the Conjugate Gradient (CG) method. It is significantly faster (40-60x) and numerically robust compared to legacy methods like the Neumann series. It takes a covariance matrix (Σ) and expected return vector (μ) to determine optimal asset weights, providing full provenance tracking for auditability.