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Found 91 Skills
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Establish or formalize your design system foundation. Create design tokens (color, typography, spacing, shadows, borders), define component architecture, document design principles, and build the structure that enables consistency and scalability. Works with Tailwind CSS and framework-agnostic approaches.
Build production-ready multi-agent AI systems with security, observability, and scalability using LangGraph and FastAPI
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".
Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).
This skill provides guidance for working with the Modal cloud platform. Use this skill whenever the user mentions Modal or has code that imports the `modal` SDK in Python, Go, or JavaScript. This skill should also trigger when the user needs to run Python code with vertical or horizontal scalability (e.g. batch jobs), needs access to GPUs (e.g. AI workloads including training and inference) or needs to run untrusted processes in a sandbox, since Modal serves these use cases well.
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.
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Principal backend engineering intelligence for C++ systems and performance-critical services. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, memory safety, latency, reliability, observability, scalability, operability.
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.
Principal backend engineering intelligence for Node.js runtime systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.