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Found 92 Skills
Conducts comprehensive backend design reviews covering API design quality, database architecture validation, microservices patterns assessment, integration strategies evaluation, security design review, and scalability analysis. Evaluates API specifications (REST, GraphQL, gRPC), database schemas, service boundaries, authentication/authorization flows, caching strategies, message queues, and deployment architectures. Identifies design flaws, security vulnerabilities, performance bottlenecks, and scalability issues. Produces detailed design review reports with severity-rated findings, architecture diagrams, and implementation recommendations. Use when reviewing backend system designs, validating API specifications, assessing database schemas, evaluating microservices architectures, reviewing integration patterns, or when users mention backend design review, API design validation, database design review, microservices assessment, or backend architecture evaluation.
Architecture reviews across 7 dimensions: structural integrity, scalability, enterprise readiness (SOC2/HIPAA/GDPR/PCI-DSS), performance, security, operational excellence, and data architecture. Produces scored reports with prioritized recommendations. Three modes: (1) Codebase review — evidence-based analysis of source code, configs, IaC; (2) Document review — risk-based analysis of design docs, RFCs, specs; (3) Hybrid — drift detection between intent and implementation. Triggers on: "review architecture", "critique design", "audit system", "evaluate codebase", "find design flaws", "assess scalability", "check security", "enterprise readiness", "architecture assessment", "technical due diligence", or when user provides a system design document or codebase and asks for feedback or improvements. For architecture diagrams, visuals, or topology drawings, use architecture-diagram instead.
Build FastAPI applications using Clean Architecture principles with proper layer separation (Domain, Infrastructure, API), dependency injection, repository pattern, and comprehensive testing. Use this skill when designing or implementing Python backend services that require maintainability, testability, and scalability.
Master plugin folder structure, manifest design, and architectural patterns. Learn to organize plugins for scalability and maintainability.
Test application performance, scalability, and resilience. Use when planning load testing, stress testing, or optimizing system performance.
Event-driven architecture patterns with event sourcing, CQRS, and message-driven communication. Use when designing distributed systems, microservices communication, or systems requiring eventual consistency and scalability.
Assess whether a project is ready for cloud-native deployment. Evaluates statelessness, config, scalability, and produces a readiness score (0-12). Use when user asks about containerization readiness, Docker/Kubernetes compatibility, deployment feasibility, whether their app can run in containers or the cloud, or wants a pre-deployment assessment. Also triggers on "/cloud-native-readiness".
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
Performance and scalability analysis specialist. Identifies algorithmic inefficiencies, N+1 queries, memory leaks, and concurrency issues. Use when reviewing loops, database queries, file I/O, or high-concurrency code.
Provides comprehensive code review covering 6 focused aspects - architecture & design, code quality, security & dependencies, performance & scalability, testing coverage, and documentation & API design. Use this skill for deep analysis with actionable feedback after significant code changes.
Build production-ready multi-agent AI systems with security, observability, and scalability using LangGraph and FastAPI
Asynchronous event-based communication to decouple producers/consumers for scalability and resilience. Triggers: event-driven, message queue, pub/sub, asynchronous, decoupling Use when: real-time workloads or multiple subsystems react to same events DO NOT use when: selecting paradigms (use architecture-paradigms first), simple request-response.