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 outlines the necessary steps for major Replit environment upgrades and data migration. It covers updating Nix channels, upgrading runtimes (Node.js, Python), and migrating critical data from Replit KV databases to PostgreSQL or Object Storage. It also details switching between deployment types, ensuring a smooth and stable transition for production applications.

This skill provides comprehensive, production-grade patterns for asynchronous programming in Rust using the Tokio runtime. It covers essential concepts like tasks, channels, and streams, along with robust error handling techniques. Use it when developing concurrent network services, building complex async applications, or optimizing performance in Rust.

A comprehensive collection of diagnostic scripts designed for troubleshooting Sentry integration failures. This bundle collects SDK versions, runtime configuration details (e.g., DSN, environment), and performs network connectivity checks to the Sentry ingest endpoint. Use it when events are not appearing in Sentry, or when systematically diagnosing complex setup issues across Node.js and Python environments.

This advanced guide provides strategies for scaling Sentry in high-traffic, enterprise-level applications handling millions of events daily. It details implementing adaptive sampling based on endpoint criticality, using adaptive error deduplication to manage quotas, and optimizing the SDK for minimal runtime overhead, ensuring robust visibility even under extreme load.

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

Scans Substrate/Polkadot FRAME pallets for seven critical vulnerabilities such as arithmetic overflow, panic DoS, incorrect weights, unsigned validation issues, bad randomness, and origin flaws, making it ideal for runtime audits and pre-launch security assessments.

Presents advanced TypeScript techniques—generics, conditional types, mapped types, discriminated unions, and utility combinations—to help developers build safer, more expressive type systems and runtime guards for complex apps.

A comprehensive guide for diagnosing and resolving common Vercel deployment errors across build, runtime, and edge network layers. Use this resource when encountering failed deployments, 500/504 errors, or issues with serverless functions. It provides detailed root cause analysis and step-by-step fixes using CLI commands and log inspection.

Guides deploying Next.js apps to Vercel covering environment variable strategy, choosing edge vs serverless runtimes, build optimizations, preview workflows, and custom domain setup to secure production releases.

This comprehensive guide catalogs common anti-patterns and mistakes when deploying applications on Vercel. It covers critical architectural areas such as secret management (preventing key exposure), optimizing serverless function performance (cold start, timeouts), adhering to Edge Runtime standards, and resolving configuration errors. Use this skill for code reviews, security audits, and onboarding new developers to ensure best deployment practices.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.