Skill UI

Automatically collects comprehensive diagnostic information for Replit workspaces, deployments, and databases. This tool gathers system state, runtime versions, environment variables, network logs, and package details. It is essential for troubleshooting persistent issues or preparing detailed evidence for support tickets.

This comprehensive guide provides detailed steps for load testing and capacity planning of Replit deployments. Learn how to benchmark performance using industry tools like k6 and autocannon, optimize Autoscale behavior, right-size Reserved VMs, and manage database connection pools. It covers optimizing cold start times and ensuring high availability under variable and peak traffic loads.

This comprehensive guide details how to configure robust multi-environment deployments (Development, Staging, Production) within the Replit ecosystem. It covers best practices for database separation, managing environment-specific secrets, implementing reliable environment detection logic, and setting up CI/CD pipelines to ensure applications are stable and secure across all deployment stages.

This comprehensive guide provides methods for implementing deep observability for Replit deployments. It covers building detailed health check endpoints (tracking uptime, memory, and DB status), setting up structured JSON logging, detecting cold start events, and configuring external alerting mechanisms (e.g., UptimeRobot, Slack). Use this when building robust, production-grade applications that require continuous performance monitoring and reliability tracking.

This guide details essential patterns for building fault-tolerant and highly available Replit applications. It covers handling cold starts, implementing graceful shutdowns (via SIGTERM), and crucially, migrating persistent application state from ephemeral local filesystems to robust external storage (like Replit KV or Object Storage). Essential for production-grade deployments.

Runs a predefined suite of smoke tests to verify key application functionality, authentication, integrations, and system health after deployments, upgrades, or configuration updates.

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

Guides building Apex logic, Lightning Web Components, SOQL tuning, integrations, and DX-powered deployments for Salesforce apps, with emphasis on bulk-safe, governor-friendly, and CI/CD-aware workflows.

This comprehensive toolkit provides end-to-end DevOps automation capabilities. It scaffolds CI/CD pipelines (GitHub Actions, CircleCI), generates robust Infrastructure as Code (IaC) modules using Terraform for major clouds (AWS, GCP, Azure), and manages complex Kubernetes deployments (Blue/Green, Rolling). Ideal for automating infrastructure setup, application deployment, and drift management in multi-cloud environments.

Integrate Sentry into your CI/CD pipeline to achieve end-to-end release tracking. This skill guides you through using the Sentry CLI and SDK to record deployments, associate commits, upload source maps, and monitor crucial release health metrics. Key functions include detecting regressions, calculating crash-free session rates, and tracking user adoption curves for every deployed version.

An expert architect specializing in designing and optimizing service mesh using industry leaders like Istio and Linkerd. This skill provides deep knowledge of cloud-native networking patterns, enabling secure, reliable microservices communication. It covers advanced topics including zero-trust networking, mTLS configuration, sophisticated traffic management (e.g., canary and blue-green deployments), observability integration, and multi-cluster federation.

Serve production-grade LLM APIs with vLLM, leveraging PagedAttention, continuous batching, quantization, and tensor parallelism to maximize throughput while keeping latency, GPU usage, and scalability under control for OpenAI-compatible deployments.