claude-skills

Identify and avoid common architectural mistakes. Recognize patterns of failure. Use when reviewing designs or learning from mistakes.

Practice architectural design with structured problems. Time-limited design challenges. Use for skill development, interviews, team exercises.

Reference catalog of proven architecture patterns. Know when to apply each pattern, tradeoffs, and examples. Use as reference when designing systems.

Lead effective architecture reviews. Manage discussions, surface disagreements, build consensus, document decisions. Use when conducting reviews or running architecture forums.

Prepare for architecture interview questions and scenarios. Practice system design, tradeoff discussions, communication. Use for career development and interview readiness.

Conduct comprehensive system architecture evaluation. Assess design quality, technical debt, operational readiness, scalability. Use when auditing existing systems.

Coach engineers on architecture and design. Provide feedback, guide learning, support growth. Use when mentoring junior architects or senior engineers.

Define foundational architecture principles and decision-making rules. Communicate guidelines across organization. Use when establishing architectural standards or updating strategy.

Standardized architecture review process with consistent criteria. Evaluate systems against principles, quality attributes, and operational readiness. Use when conducting architecture reviews.

Predict impacts of architectural changes on dependent systems. Trace dependencies, assess risk, plan rollout. Use when making major changes or analyzing blast radius.

Implement compliance requirements (SOC2, GDPR, HIPAA). Design architecture for regulations. Map technical controls to compliance requirements. Use when building regulated systems.

Manage and optimize system dependencies (libraries, services, data). Reduce coupling, track vulnerabilities, plan deprecation. Use when managing dependency sprawl or improving system modularity.

Design metrics that measure architecture health (coupling, modularity, scalability). Automate fitness function checks in CI/CD. Use when establishing quality gates or measuring architectural degradation.

Measure, prioritize, and address technical debt. Classify debt by impact and effort. Build paydown roadmap. Use when evaluating system health or planning refactoring.

Write concise executive summaries of architecture. Communicate decisions and tradeoffs to non-technical stakeholders. Use when presenting to leadership or documenting decisions.

Create multi-quarter architecture vision and roadmap. Prioritize initiatives, estimate effort, communicate strategy. Use when planning architecture evolution or communicating long-term vision.

Create C4 context, container, component, and code diagrams. Visualize architecture at different abstraction levels. Use when communicating system structure to diverse audiences.

Design high-level system boundary diagrams. Show external systems, users, data flows. Use when onboarding teams or clarifying system scope.

Propose architectural changes with comprehensive RFCs. Structure feedback, document decisions, track rationale. Use when proposing major architecture changes or design patterns.

Show interactions and flows over time. Illustrate request paths, asynchronous patterns, error handling. Use when documenting complex flows or onboarding on system behavior.

Present architecture to diverse audiences (executives, product, engineers). Tailor message, manage Q&A, handle skepticism. Use when communicating major decisions or visions.

Design data movement and transformation pipelines. Show how data flows between systems, transforms, and where it's stored. Use when architecting data integrations or ETL processes.

Establish data ownership, quality standards, compliance policies, and metadata management. Build organizational data practices. Use when defining data strategy or improving data quality and compliance.

Design data models for business domains using entity-relationship or domain-driven design. Use when modeling new domains or refactoring data structures.

Design batch and streaming data pipelines. Plan ingestion, transformation, quality checks, and failure recovery. Use when building ETL/ELT systems or data infrastructure.

Store state as immutable event log instead of current values. Build auditable, event-driven systems with full history. Use when auditability, temporal queries, or event-driven processing matters.

Integrate data across systems using ETL, CDC, event streaming, and API patterns. Design robust, maintainable data integrations. Use when connecting disparate data sources or microservices.

Manage database schema changes while maintaining backwards compatibility. Handle migrations, versioning, and zero-downtime deployments. Use when evolving data models in production systems.

Choose the right database technology for specific workloads. Evaluate relational, NoSQL, data warehouses, and search engines. Use when selecting storage systems for new features or optimizing existing ones.

Document architectural decisions using ADR format. Use when making significant architectural choices that affect future development.

Systematically evaluate whether to build a solution or buy/use existing product. Use when evaluating tooling, services, or frameworks.

Modernize legacy systems without rewriting from scratch. Use strangler fig, facade, and strategic refactoring.

Plan large-scale migrations from old systems to new architectures. Use when modernizing legacy systems or upgrading infrastructure.

Plan and execute focused POCs to validate architectural decisions before full commitment.

Identify and manage architectural risks using risk matrices and mitigation strategies.

Evaluate and track technology choices on a radar. Use when selecting frameworks, databases, or tools.

Systematically evaluate and select vendors for infrastructure, SaaS, or services.

Forecast infrastructure resource needs (compute, storage, network). Model growth scenarios and utilization targets. Use when planning infrastructure investments or optimizing costs.

Design cloud infrastructure for AWS, GCP, or Azure. Plan compute, storage, networking, and compliance. Use when architecting cloud systems or migrating to cloud.

Design Kubernetes clusters for scaling, service discovery, storage, and networking. Plan upgrades and multi-cluster strategies. Use when architecting container infrastructure.