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Found 529 Skills
Build scalable customer support systems including help centers, chatbots, ticketing systems, and self-service knowledge bases. Use when designing support infrastructure, reducing support load, improving customer satisfaction, or scaling support without linear hiring.
AAA-caliber engineering council for building production-quality games. Use when implementing game systems, writing game code, designing data architecture, building UI components, creating tutorials, optimizing performance, or any technical game development task. Covers game programming patterns, casino/card game implementation, reward systems, game UI engineering, tutorial design, code architecture, data infrastructure, security, and quality assurance. Triggers on requests for game code, system implementation, refactoring, performance optimization, data design, or technical architecture decisions.
Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems. Designs and implements scalable ML systems with focus on reliability and performance.
Expert in Kanidm modern identity management system specializing in user/group management, OAuth2/OIDC, LDAP, RADIUS, SSH key management, WebAuthn, and MFA. Deep expertise in secure authentication flows, credential policies, access control, and platform integrations. Use when implementing identity management, SSO, authentication systems, or securing access to infrastructure.
Comprehensive Azure cloud expertise covering all major services (App Service, Functions, Container Apps, AKS, databases, storage, monitoring). Use when working with Azure infrastructure, deployments, troubleshooting, cost optimization, IaC (Bicep/ARM), CI/CD pipelines, or any Azure-related development tasks. Provides scripts, templates, and best practices for production-ready Azure solutions.
GitOps workflows and patterns using ArgoCD and Flux for declarative Kubernetes deployments. Use when implementing CI/CD for Kubernetes, managing multi-environment deployments, or adopting declarative infrastructure practices.
AWS CloudFormation patterns for ECS clusters, services, and task definitions. Use when creating ECS infrastructure with CloudFormation, configuring container definitions, scaling policies, service discovery, load balancing integration, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and blue/green deployments with CodeDeploy.
You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.
Generate interactive graph visualizations in the browser from any data - codebases, infrastructure, relationships, knowledge maps
Prometheus monitoring and alerting for cloud-native observability. USE WHEN: Writing PromQL queries, configuring Prometheus scrape targets, creating alerting rules, setting up recording rules, instrumenting applications with Prometheus metrics, configuring service discovery. DO NOT USE: For building dashboards (use /grafana), for log analysis (use /logging-observability), for general observability architecture (use senior-software-engineer with infrastructure focus). TRIGGERS: metrics, prometheus, promql, counter, gauge, histogram, summary, alert, alertmanager, alerting rule, recording rule, scrape, target, label, service discovery, relabeling, exporter, instrumentation, slo, error budget.
Guides the creation of self-signed SSL/TLS certificates using OpenSSL, including key generation, certificate creation, combined PEM files, and verification scripts. This skill should be used when tasks involve generating self-signed certificates, creating SSL certificate infrastructure, or writing certificate verification scripts.
Build trading systems in the style of Two Sigma, the systematic investment manager pioneering machine learning at scale. Emphasizes alternative data, distributed computing, feature engineering, and rigorous ML infrastructure. Use when building ML pipelines for alpha research, feature stores, or large-scale backtesting systems.