Skill UI

A robust pattern for configuring API clients to interact with Linear across multiple isolated environments (Development, Staging, Production). This setup ensures that environment-specific credentials (API keys, webhooks) are securely loaded from secret managers (Vault, AWS, GCP) and implements environment guards to prevent accidental operations in the wrong environment. Ideal for professional CI/CD pipelines.

A comprehensive guide and implementation using TypeScript to establish production-grade observability for Linear API integrations. This includes defining Prometheus metrics (tracking latency, error rates, rate limits, and webhooks), implementing structured logging (using pino), and setting up robust health check endpoints. It ensures full visibility into API performance and system health, critical for mission-critical SaaS applications.

A comprehensive guide to optimizing interactions with the Linear API. Learn advanced techniques to improve application performance by eliminating N+1 queries, implementing robust caching strategies for static data (e.g., teams, states), managing cache invalidation via webhooks, and efficiently batching multiple mutations into single GraphQL requests. Essential for building scalable, low-latency SaaS applications.

This comprehensive checklist provides best practices and patterns for deploying Linear integrations to a production environment. It covers critical areas including robust authentication and secure secret management, error handling and system resilience (rate limiting, timeouts), performance optimization (caching, batching), and setting up full observability (health checks, structured logging). Use this when preparing for a go-live or auditing existing deployments.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

This guide provides production-grade architectural patterns for integrating with the Linear API. It helps developers choose the optimal strategy—be it Simple SDK calls, Service-Oriented Gateways with caching, or Event-Driven Webhooks—based on system complexity, scale, and real-time requirements. Includes patterns like CQRS and rate limiting best practices.

A comprehensive guide covering advanced patterns for the Linear SDK using TypeScript/JavaScript. Learn how to implement efficient cursor-based pagination, avoid N+1 query problems by batch-loading relations, perform complex filtering, and handle API calls with type-safe error management when integrating with the Linear GraphQL API.

This guide details secure methods for connecting external applications to the Linear API. It covers essential security practices, including secure storage of API keys using environment variables, implementing OAuth 2.0 with PKCE for robust authentication, managing token refresh cycles, and verifying webhook signatures using HMAC-SHA256. Essential for building production-grade, secure integrations.

This comprehensive guide outlines a multi-step process for safely upgrading the Linear SDK across major versions. It details how to check for breaking changes (like renamed fields or changed return types), implement compatibility layers using patterns like the Bridge pattern in TypeScript, run thorough unit and integration tests, and establish a robust deployment and rollback plan, ensuring zero downtime migration.

A comprehensive framework for setting up and processing Linear webhooks. This skill demonstrates best practices for real-time event handling, including mandatory HMAC signature verification, replay attack prevention, and robust event routing for various data types (Issues, Comments, Projects). Ideal for building reliable, real-time integrations between Linear and external systems.

Guides symbolic mathematics work in Python, covering algebraic simplification, calculus, equation solving, linear algebra, physics, number theory, geometry, and code generation to deliver exact symbolic results instead of numeric approximations.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.