Skill UI

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.

This guide details advanced methods for merging multiple specialized LLMs (e.g., combining math, coding, and chat capabilities) without the need for expensive retraining. Learn various techniques such as Linear Merging, SLERP, Task Arithmetic, TIES-Merging, and DARE. Use mergekit to create high-performing, specialized model variants quickly, significantly improving performance and reducing computational costs.

Enables regression modeling via the regression-analysis-tool plugin by analyzing datasets, choosing linear or polynomial fits, validating models, and reporting metrics so users can predict outcomes or explore variable relationships.

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

This secondary workflow automates post-meeting follow-up processes. It intelligently extracts action items, inferring priority and assignees, and synchronizes these tasks to external project management tools (e.g., Asana, Jira, Linear). It also generates and sends follow-up emails, creating a complete, automated task lifecycle management system.

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

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.