Skill UI

This SDK provides a comprehensive client for building advanced search applications using Azure AI Search with TypeScript. It supports cutting-edge retrieval methods including Vector Search, Hybrid Search (combining keywords and vectors), and Semantic Search. Ideal for developers building e-commerce, documentation, or knowledge retrieval systems requiring deep search capabilities.

This comprehensive workflow automates the creation of C4 architectural documentation for any codebase. It utilizes a bottom-up analysis approach, starting by analyzing code details (Code Level), synthesizing them into logical components, mapping containers, and finally establishing the high-level system context. Ideal for documenting complex, existing systems and ensuring comprehensive technical knowledge transfer.

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

Claudeception continuously monitors work sessions, looking for reusable insights—debugging tricks, tool integrations, and workflow optimizations—and codifies them into new Claude Code skills so teams retain non-obvious knowledge for future tasks.

This skill helps transform code reviews from simple gatekeeping into a powerful knowledge-sharing mechanism. It guides users through systematic analysis of code changes, focusing on correctness, security, performance, and maintainability. Use it when reviewing pull requests, establishing best practices, or mentoring developers to ensure high-quality, robust codebase development.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

This skill provides expert guidance for modern, multi-cloud database administration. It covers comprehensive knowledge across AWS, Azure, and GCP platforms, spanning relational, NoSQL, and graph databases. Key areas include Infrastructure as Code (Terraform, Flyway), achieving high availability, disaster recovery, advanced performance optimization, and ensuring strict regulatory compliance (HIPAA, GDPR). It is essential for managing complex, highly reliable cloud data architectures.

This skill automates the creation of comprehensive documentation for relational databases (PostgreSQL/MySQL). By introspecting live schemas, it extracts detailed metadata including table structures, column descriptions, primary/foreign key relationships, indexes, views, and stored procedures. The output provides a data dictionary, entity-relationship map, and schema statistics, crucial for knowledge transfer and database lifecycle management.

This expert provides advanced, practical knowledge across the entire lifecycle of containerized applications. Core expertise includes multi-stage Dockerfile optimization, container security hardening (non-root users, minimal attack surface), and advanced orchestration using patterns like Docker Compose. Ideal for ensuring robust, secure, and production-ready deployments.

This skill analyzes existing codebases to create comprehensive, long-form technical documentation. It captures the architecture, design patterns, implementation details, and rationale (the 'why') behind complex systems. Ideal for creating definitive technical manuals, system overviews, and internal knowledge bases for onboarding and maintenance.

This comprehensive workflow bundle guides the entire documentation lifecycle for software projects. It covers planning, API specification (OpenAPI), architectural design (C4 model), code commenting (JSDoc/TSDoc), generating READMEs, and maintaining structured knowledge bases (Wikis, Changelogs). Use this system when documenting a new product, maintaining an evolving codebase, or creating comprehensive technical guides.

A comprehensive guide to selecting and implementing the optimal Firecrawl architecture pattern for various data ingestion needs. Learn to choose between On-Demand, Scheduled Crawl Pipelines, and Real-Time Ingestion Pipelines based on data volume, required latency, and complexity. This is essential for designing scalable, production-grade scraping infrastructure, including RAG and knowledge base applications.