Skill UI

A comprehensive guide detailing best practices for optimizing Angular applications. It covers critical topics like change detection strategies (OnPush, Signals), managing asynchronous data flows (avoiding waterfalls), optimizing bundle size via lazy loading and tree shaking, and improving rendering efficiency with modern features like @defer and trackBy. Use this when building, reviewing, or refactoring complex Angular components for peak performance.

Browser automation expert for debugging flaky tests and running long-lived scrapers using Playwright/Puppeteer, focusing on robust selectors, auto-waits, isolation, and anti-detection practices.

Scans component namespaces to detect duplicated domain logic across services, then suggests consolidation paths or shared services to improve maintainability and clarity.

Detect SQL injection weaknesses by running the sql-injection-detector plugin against codebases, identifying risky queries, and providing remediation guidance to secure applications.

Provides production-ready patterns for integrating with the Guidewire insurance platform. This SDK abstracts the complexity of hybrid APIs (REST/SOAP), offering a structured client, robust error handling (retry logic, conflict detection), and a fluent query builder for consistent data access across policies and claims.

Detects fail-open insecure defaults such as hardcoded secrets, weak auth, or permissive security that allow production apps to run unsafely, guiding auditors through discovery, verification, confirmation, and reporting steps during security reviews or deployment validation.

This comprehensive checklist guides the operational validation of the Juicebox AI talent search and analysis platform in a production environment. It ensures system reliability, data security, and service continuity by reviewing critical areas such as API key management, rate limit handling, robust error detection (circuit breakers, exponential backoff), data encryption (GDPR/CCPA compliance), and comprehensive monitoring setup. It is essential for preventing service failures and maintaining search data integrity.

This comprehensive guide assists developers in planning and executing upgrades for systems utilizing the Juicebox API. It covers critical steps such as API version detection, migrating complex data schemas (e.g., search results), updating query syntax, and implementing robust rollback strategies. Essential for maintaining continuous operation in recruiting and market research applications when the underlying API changes.

A comprehensive guide to minimizing API usage costs by implementing advanced integration patterns. Learn to reduce request volume through response caching with TTL, replacing expensive polling with webhook-driven sync, and utilizing smart polling with change detection filters. Also covers usage tracking and request deduplication for highly efficient system architecture.

This skill provides a comprehensive guide to implementing observability for MaintainX API integrations. It covers best practices for structured logging, Prometheus metrics collection (Counters, Histograms, Gauges), API request tracing using Axios interceptors, and setting up health and alerting endpoints. Use this when you need to monitor API reliability, detect performance degradation, and ensure rapid issue detection in your connected services.

This guide provides a comprehensive roadmap for migrating and upgrading integrations with the OpenEvidence clinical AI platform. It addresses critical API changes, such as schema restructuring (e.g., flat answers to structured evidence), version detection, and updated citation formats. Developers can use this knowledge to implement robust migration logic, ensuring that downstream clinical applications maintain high levels of accuracy and safety when accessing evolving medical evidence.

This skill provides comprehensive tools to monitor the real-time availability, status, and performance of various large language models (LLMs) accessed via OpenRouter. It supports active health probes, catalog checks, and scheduled monitoring via Python and Bash, ensuring robust fault detection and failover logic for production systems that rely on external AI APIs.