Skill UI

This guide provides a comprehensive workflow for deploying applications powered by HubSpot to major cloud platforms, including Vercel, Fly.io, and Google Cloud Run. It covers best practices for secret management (like API tokens), setting up environment variables, and implementing robust health check endpoints to ensure continuous operation in production environments.

A comprehensive guide and toolset for load testing HubSpot integrations and planning for high-volume traffic. This resource details HubSpot's specific API rate limits (per-second and daily) and provides k6 scripts and capacity calculators to ensure your application scales reliably before deployment in production.

This guide details best practices for setting up and managing HubSpot integrations across development, staging, and production environments. It emphasizes the critical use of environment-specific access tokens, robust secret management (Vault, AWS/GCP SM), and implementing safety guards within CI/CD pipelines to ensure data integrity and prevent cross-environment deployment errors.

A comprehensive checklist and guide for securely deploying HubSpot CRM integrations to a production environment. It covers mandatory pre-deployment verification steps (like token scoping and secure storage), implementing robust health check endpoints, configuring critical monitoring alerts (rate limits, 401/429 errors), and executing post-deployment validation scripts to ensure system stability and reliability upon launch.

This comprehensive guide assists developers in upgrading the @hubspot/api-client SDK. It covers migrating between major API versions (e.g., v1 to v3, v3 to v4), addressing key breaking changes, and implementing modern authentication patterns, such as transitioning from API Keys to Access Tokens. Use this when integrating or maintaining HubSpot-dependent applications.

This elite skill acts as an art director and implementation strategist for modern web development. It mandates an 'Image-First' workflow: first generating high-fidelity, premium design images for specific sections (e.g., hero, product features), deeply analyzing these visual references, and only then translating them into clean, structured, and highly implementable frontend code. It is designed to avoid generic AI clichés, ensuring results that are aesthetically superior, structurally sound, and ready for professional deployment.

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

This guide provides comprehensive, step-by-step instructions for deploying Intercom-powered applications and webhooks to major cloud platforms like Vercel, Fly.io, and Google Cloud Run. It covers secure secret management, configuring platform-specific webhook endpoints, and implementing robust health checks, ensuring seamless and resilient production integration for messaging and support systems.

A comprehensive guide for setting up Intercom across development, staging, and production environments. This method ensures proper environment isolation, manages environment-specific access tokens and webhooks, and implements safety guards to prevent accidental data modification in critical workspaces. Essential for secure CI/CD deployments of SaaS applications.

A comprehensive checklist for ensuring Intercom integrations are robust and production-ready. It covers critical areas such as authentication, error handling (rate limits, retries), secure webhook validation, data privacy (PII, GDPR), and monitoring setup. Includes sample TypeScript and Bash scripts for pre-flight verification and graceful rollback procedures.

This structured skill guides users through root-cause-first debugging when an implementer is blocked, verification fails, or repeated attempts at fixing do not converge. It mandates a systematic approach combining local evidence inspection, runtime analysis, and external documentation research to accurately classify the failure's true source (e.g., missing dependency, spec conflict). It prevents speculative patching and generates a clear, actionable fix plan.

This tool systematically converts a project roadmap into actionable specifications. It first analyzes features by grouping them into dependency waves. It then automatically dispatches sub-agents to generate complete feature specs (requirements, design, tasks, and JSON) for each wave. The core value lies in its comprehensive cross-spec review, which verifies data model consistency, interface alignment, and architectural integrity across the entire project, ensuring a cohesive final product.