Skill UI

A comprehensive template for developing modern, full-stack applications. This guide covers architecture overview, file structure, and best-practice code patterns for advanced technology stacks including Next.js, FastAPI, Supabase, and structured AI integration using Claude API. It serves as a robust blueprint for professional development teams.

A comprehensive technical guide detailing essential patterns and techniques for analyzing compiled binaries. It covers foundational assembly concepts across x86-64, ARM architectures, and provides structured examples for function prologues, calling conventions, control flow (loops, branches), data structure access, and common arithmetic/string operations. Essential for reverse engineering and low-level systems programming.

This guide provides production-ready patterns for building robust integrations with the Clay platform. It covers interacting with Clay via webhooks (inbound data), HTTP API enrichment columns (outbound data), and the Enterprise API. Implementation examples are provided in TypeScript and Python, ensuring reliable, reusable, and scalable data pipeline construction.

A comprehensive guide demonstrating how to implement robust authentication using Clerk across various modern web environments, including Next.js Server Components, Client Components, API routes, and Server Actions. Learn best practices for securing endpoints, managing session data, and handling authenticated requests from end-to-end.

A specialist tool designed to generate, format, and validate comprehensive technical documentation. It supports multiple standards, including Google/NumPy docstrings (for Python), JSDoc (for TypeScript), and full OpenAPI/Swagger specifications. Key features include automated detection of code languages and frameworks, robust validation of code examples (using tools like doctest and OpenAPI linters), and generating structured user guides or site configurations.

A comprehensive guide to structuring effective custom prompts for AI coding assistants like Cursor. Learn the four components of prompt anatomy (Context, Task, Constraints, Format), and utilize reusable templates for complex tasks such as feature implementation, bug fixing, code review, and test generation. Master advanced techniques like Chain of Thought and Few-Shot examples to ensure high-quality, consistent code output.

This guide provides minimal working examples for Deepgram speech-to-text transcription. It demonstrates how to transcribe audio from a URL or a local file using both TypeScript and Python. Users can learn basic API patterns, implement advanced features like diarization (speaker identification), and compare various model options (e.g., nova-3, nova-2). Ideal for initial integration and testing.

Automates guidance when requesting design doc templates, covering patterns, best practices, and example triggers across API, user, and architecture docs to streamline technical documentation.

A comprehensive guide for migrating from Documenso's legacy v1 API (document-centric) to the modern, recommended v2 API (envelope-based). This resource covers SDK upgrades for TypeScript and Python, providing code migration examples, best practices for implementing feature flags for gradual rollout, and steps for self-hosted container version upgrades.

Automates guidance for crafting ECS task definitions within AWS, offering best practices, validation, and production-ready examples whenever you invoke the ecs task definition creator or request related compute, storage, or networking advice.

A comprehensive guide providing a minimal working example for integrating with the Evernote Cloud API. It covers basic note creation, demonstrates the required ENML (Evernote Markup Language) format, and teaches core operations like listing notebooks and retrieving note details. Ideal for developers starting a new integration or testing API setup.

Provides comprehensive strategies and code examples for managing API rate limits when integrating with the Evernote platform. Techniques include implementing exponential backoff, wrapping clients with minimum delays, batching operations, and optimizing queries (e.g., using metadata only) to ensure robust, high-throughput, and stable API usage.