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Found 38 Skills
Design software architectures with appropriate patterns for scale, maintainability, and team structure. Covers layered, hexagonal, event-driven, CQRS, and modular monolith architectures. Produces architecture decision records, component diagrams, and dependency maps. Prevents over-engineering, premature distribution, and architectural drift.
Use when designing system architecture, making high-level technical decisions, or planning major system changes. Focuses on structure, patterns, and long-term strategy.
Create and validate solution design documents (SDD). Use when designing architecture, defining interfaces, documenting technical decisions, analyzing system components, or working on solution-design.md files in docs/specs/. Includes validation checklist, consistency verification, and overlap detection.
Use ONLY when creating NEW registrable components in ML projects that require Factory/Registry patterns. ✅ USE when: - Creating a new Dataset class (needs @register_dataset) - Creating a new Model class (needs @register_model) - Creating a new module directory with __init__.py factory - Initializing a new ML project structure from scratch - Adding new component types (Augmentation, CollateFunction, Metrics) ❌ DO NOT USE when: - Modifying existing functions or methods - Fixing bugs in existing code - Adding helper functions or utilities - Refactoring without adding new registrable components - Simple code changes to a single file - Modifying configuration files - Reading or understanding existing code Key indicator: Does the task require @register_* decorator or Factory pattern? If no, skip this skill.
Transform code into clean, testable architecture using SOLID principles, Clean Architecture, and proven design patterns
Creates technical architecture and system design.
Generate implementable architecture solutions based on business requirements and tech stacks, providing structured suggestions and tech stack selection optimization through a four-step process (information collection, requirement sorting, iterative improvement, solution output)
[Architecture] Full solution architecture: backend + frontend patterns, design patterns, library ecosystem, CI/CD, deployment, monitoring, testing, code quality, dependency risk. Compare top 3 approaches per concern with recommendation.
Use when designing new system architecture, reviewing existing designs, or making architectural decisions. Invoke for system design, architecture review, design patterns, ADRs, scalability planning.
Use when designing system architecture, choosing between monolith/microservices/serverless, planning scalability, or making technology decisions. Covers microservices, event-driven, CQRS, modular monoliths, distributed systems, and reliability patterns for production-grade software.
Daily coding assistant that auto-triggers when writing/modifying code, providing a core checklist. ✅ Trigger scenarios: - Implementing new features, adding code, modifying existing code - User requests "write a...", "implement...", "add...", "modify..." - Any coding task involving Edit/Write tools ❌ Does not trigger: - Pure reading/understanding code (no modification intent) - Already covered by specialized skills (bug-detective, architecture-design, tdd-guide) - Configuration file changes, documentation writing
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.