Skill UI

Showcases modern Python practices, covering strict 3.12 type hints, dataclasses vs Pydantic decisions, asyncio/httpx patterns, src-layout projects, pyproject tool configs, virtual env workflows, and pytest guidance for reliable codebases.

This guide identifies and explains common anti-patterns and critical pitfalls encountered when building applications on the Replit platform. Topics include data persistence, secret management, networking bindings, and environment configuration. Use this resource when conducting code reviews, onboarding new developers, or auditing existing Replit applications for best practices.

This guide identifies and provides remedies for common anti-patterns when configuring and using the Sentry SDK in production environments. It covers critical pitfalls like hardcoded DSNs, incorrect sample rate settings, and failure to flush events, ensuring data integrity, preventing cost overruns, and maintaining comprehensive monitoring coverage across various platforms.

This pattern provides established best practices for implementing efficient similarity search in production systems. It is crucial for building semantic search engines, implementing Retrieval-Augmented Generation (RAG), creating advanced recommendation systems, and optimizing retrieval performance when scaling to millions of vectors.

This skill provides comprehensive guidelines for architecting and developing high-quality, maintainable software. It is deeply rooted in Clean Architecture and Domain-Driven Design (DDD) principles. It covers best practices for code decomposition, naming conventions, separation of concerns, and avoiding common anti-patterns. Use this skill when designing, analyzing, or developing complex, domain-specific software systems.

Comprehensive TDD workflow guidance covering the Red/Green/Refactor rhythm, Arrange-Act-Assert test structure, Jest/Vitest, pytest, and Go patterns, coverage rules, mocking advice, and anti-pattern warnings to keep teams disciplined across languages.

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 outlines common anti-patterns and critical mistakes when using the Windsurf AI IDE. It covers best practices for prompt engineering, managing workspace scope (especially in monorepos), distinguishing between Supercomplete and Cascade usage, and ensuring safe, maintainable AI-assisted coding workflows. Essential for new developers and team auditors.

Monitor the adoption, feature usage, and overall Return on Investment (ROI) of AI tools like Windsurf. This skill provides comprehensive analytics, including daily active users, completion acceptance rates, and estimated time saved. It allows teams to set up usage alerts, identify underutilized seats, and build custom extensions to measure deep usage patterns, ensuring maximum team efficiency and quantifying AI impact.

Automatically monitors YAML workflows to validate syntax, infer schemas, convert formats, and harden Kubernetes/Docker Compose/CI configurations while spotting anti-patterns and security gaps.

Systematically audit Playwright test files against documented anti-patterns, rate their quality, and summarize findings so teams can fix flaky or under-tested scenarios before release.

Analyzes behavioral reports from security sandboxes (Cuckoo, AnyRun) to detect advanced anti-analysis and evasion techniques. It specifically identifies indicators such as timing checks, hypervisor artifact queries, user interaction patterns, and environment fingerprinting. This tool is essential for threat hunting, enhancing detection rules, and conducting deep malware triage.