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Implements robust Role-Based Access Control (RBAC) for enterprise Deepgram deployments. This skill demonstrates how to map various application roles (Admin, Developer, Analyst, etc.) to specific Deepgram API key scopes. It covers creating scoped keys, managing key lifecycles, and implementing middleware to enforce permissions, ensuring secure, auditable, and granular access control for team-based applications.

This skill provides a comprehensive and isolated setup for managing Deepgram API integrations across different deployment stages (Development, Staging, Production). It includes typed configuration management, a client factory pattern, and Docker Compose profiles, ensuring that each environment uses specific credentials, models, and feature settings (like diarization or PII redaction) without conflict. Ideal for robust, multi-stage application deployment.

A comprehensive checklist and guide for deploying Deepgram integrations into a production environment. It covers critical areas including robust authentication practices, system resilience design (e.g., backoff strategies, circuit breakers), performance optimization (singleton clients, audio preprocessing), security measures (PII redaction), and mandatory monitoring setup (Prometheus metrics and alert rules). Essential for any go-live phase.

A powerful tool for comprehensive, offline dependency auditing across 8+ programming ecosystems (e.g., Node, Python, Go, Rust). It identifies critical CVE vulnerabilities, license conflicts, and calculates safe upgrade paths, making it essential for pre-release security checks, legal compliance reviews, and major version migrations.

This skill functions as a specialized dependency security expert. It analyzes project dependencies to identify critical security vulnerabilities, licensing compliance issues, and outdated packages. It provides comprehensive, actionable remediation strategies to strengthen supply chain security.

A comprehensive guide for mastering major dependency version upgrades in software development. This skill covers compatibility analysis, implementing staged upgrade strategies, resolving dependency conflicts, and setting up robust testing matrices (Unit, Integration, E2E) to ensure a smooth and reliable modernization process for any codebase.

Automates dependency vulnerability checks within the Security Fundamentals skill set, guiding users through best practices, secure coding patterns, and remediation-ready output validation.

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

Automate deploying production-ready monitoring stacks like Prometheus, Grafana, and Datadog, delivering configuration files, deployment scripts, and guided instructions for infrastructure-focused observability setups.

Expert in modern CI/CD pipelines, GitOps workflows, and platform engineering. Specializes in designing and implementing secure, zero-downtime deployment strategies for enterprise-scale applications. Proficient with Kubernetes, ArgoCD, Terraform, and advanced security compliance practices.

A comprehensive guide to deployment patterns and strategies for modern cloud environments. Covers techniques like Blue/Green, Canary, and Rolling Deployments, ensuring zero-downtime upgrades when deploying to Kubernetes, Docker, or various cloud providers. Includes best practices for health checks, logging, and rollback planning.

This guide outlines advanced architectural patterns for building multi-stage CI/CD pipelines. It covers best practices for implementing robust deployment gates, including manual approvals, time-based triggers, and multi-approver workflows. It details various deployment strategies like Rolling, Blue-Green, and Canary releases, ensuring controlled, zero-downtime, and highly reliable software releases.