Skill UI

A comprehensive guide for designing and building large-scale, resilient platforms using the evolutionary modular monolith approach. It enforces strong logical boundaries using DDD principles (bounded contexts, flat-by-aggregate) and implements necessary patterns like Anti-Corruption Layers and transactional outboxes. Use this when defining a system's structure, choosing between monolith and microservices, or ensuring robust decoupling from external vendors (e.g., ERP, AI).

This advanced skill initializes the Software Development Documentation (SDD) context within a project. It detects the entire technical stack, conventions, testing tools, and persistence mode across all discovered projects. It then structures and persists this comprehensive context, including building a skill registry and determining the strict TDD status, ensuring a robust starting point for development.

Apply Vaughn Vernon-inspired rules for building aggregates, domain events, repositories, and application services. This skill helps structure code using bounded contexts and domain logic, ensuring clean architecture and maintainable software design during development.

This toolkit provides six production-ready AI engineering workflows including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior engineering partners with structured methodologies, quantitative scoring, and checklists for consistent results in LLM system optimization and security.

This skill enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding. It applies First Principles and 5 Whys at a micro scale to maintain a stable mental model. Used for security auditing, architecture review, and threat modeling to reduce hallucinations and ensure bottom-up understanding.

Autonomous agents are AI systems capable of independent goal decomposition, action planning, tool execution, and self-correction without constant human guidance. This skill focuses on ensuring reliability in agent loops like ReAct and Plan-Execute, managing context usage, and handling compounding error rates. It emphasizes building constrained, domain-specific agents for production reliability rather than unrestricted autonomy.

This skill specializes in creating and maintaining Chrome Extensions using Manifest V3 architecture. It covers essential components like service workers, content scripts, and popup interfaces. The expert ensures secure cross-context communication and implements best practices for permissions and storage. Ideal for migrating legacy extensions or building new production-ready tools within the Chrome ecosystem.

Configure CircleCI 2.1 workflows, orbs, executors, caching, workspaces and parallelism for building, testing and deploying applications. Covers approvals, scheduled runs, branch filtering and secrets contexts.

This skill specializes in advanced AI context engineering, managing dynamic information flows, vector databases, and knowledge graphs. It assists in building intelligent memory systems, optimizing context windows, and orchestrating multi-agent workflows. Ideal for enterprise-scale AI applications requiring coherent state management, RAG implementation, and semantic retrieval strategies to enhance system performance and reliability.

This skill covers persistent memory systems for Large Language Model (LLM) conversations. It includes implementing short-term, long-term, and entity-based memory to enhance context retention across sessions. Features include memory consolidation, retrieval strategies, and integrating memory into prompts. Suitable for building conversational AI applications using tools like Mem0, LangChain, or Redis.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

This comprehensive guide covers best practices for building robust database systems. It teaches crucial concepts like schema normalization, effective indexing strategies, selecting the right database (e.g., SQLite vs PostgreSQL) and ORM, and optimizing queries to prevent common performance issues like N+1 problems. It emphasizes making context-aware architectural decisions.