Skill UI

A comprehensive guide to enhancing Clerk authentication performance within a Next.js application. Techniques covered include optimizing middleware routing, implementing React's cache() and unstable_cache() for data deduplication, optimizing token handling, utilizing dynamic imports for lazy loading, and leveraging Edge Runtime for faster cold starts. Ideal for reducing latency and improving user experience during authentication flows.

This comprehensive guide outlines essential patterns and best practices for integrating the Clerk SDK into modern applications. It covers crucial components like server-side and client-side authentication flows, protecting routes with middleware, implementing organization-aware data queries, and generating custom JWT tokens for secure third-party integrations. Ideal for developers building robust, secure, and scalable applications.

This comprehensive guide outlines essential, production-grade security best practices for implementing Clerk authentication. It covers securing environment variables, hardening middleware with crucial security headers (like CSP, X-Frame-Options), protecting API routes with granular authorization checks, and verifying webhooks using signature validation (svix). Use this when deploying or auditing an application to ensure maximum security posture.

Provides a comprehensive, step-by-step guide and scripts for safely upgrading Clerk SDK packages. This tool helps developers manage version upgrades and critical breaking changes (e.g., async auth calls, middleware renaming, import path changes), ensuring a smooth and reliable transition to the latest SDK version.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

CLIP enables general-purpose vision-language tasks through zero-shot image classification, similarity scoring, semantic search, and content moderation using pretrained contrastive embeddings without fine-tuning.

This skill automatically generates comprehensive and highly structured Pull Request (PR) descriptions directly from Git diffs. It goes beyond simple summaries by automatically adding detailed change categories (e.g., source, test, config), security risk assessments, and required review checklists based on the modified file types. Use this when submitting complex code changes or when the diff is large and requires explicit structure for thorough review.

This skill guides the execution of development tasks defined in a structured implementation plan, strictly following the Test-Driven Development (TDD) workflow. It manages the entire development lifecycle, including pre-flight checks, context loading, task identification, writing failing tests, implementing minimal code, refactoring, committing changes, and performing phase-wise verification. Ideal for managing complex, structured software development projects.

This skill is designed to configure a Rails project workspace to function correctly within Conductor, a platform for parallel coding agents. It sets up the necessary scaffolding, including `conductor.json`, executable setup scripts, and server scripts. Additionally, it updates critical Rails configuration files (such as Sidekiq, Cable, and development environment settings) to ensure proper environment variable handling, Redis isolation, and port management for reliable parallel agent execution.

Addresses the critical challenge of managing extremely long conversation histories (millions of tokens) in advanced AI agents, such as coding assistants. This technique moves beyond simple token minimization by implementing structured compression strategies (like Anchored Iterative Summarization) to prevent the loss of critical technical details—such as file paths, decisions, and error messages—ensuring the agent maintains functional context across complex, multi-step tasks. It emphasizes optimizing for 'tokens-per-task' rather than just tokens-per-request.

This skill analyzes the predictable ways language models lose performance as context length increases. It details core patterns, including 'lost-in-middle,' context poisoning, and distraction. Use this knowledge to diagnose unpredictable agent failures, design highly reliable systems for long document processing, and implement advanced context engineering strategies.

This tool optimizes the codebase indexing within Cursor. It processes your entire codebase, converting code chunks into vector embeddings stored in a vector database. This enables advanced @Codebase semantic search, allowing users to ask natural language questions (e.g., "Where is authentication handled?") and retrieve the most relevant code snippets, significantly improving AI context awareness across chat and coding tasks.