Skill UI

This skill defines a senior engineer specializing in the entire software development lifecycle (SDLC). It covers building robust CI/CD pipelines (e.g., GitHub Actions), containerizing applications with Docker, and managing infrastructure using Infrastructure as Code (IaC) tools like Terraform or Pulumi. Key areas include Kubernetes deployment (GitOps), cloud platform configuration (AWS/GCP/Azure), release automation, and incident response planning.

Comprehensive guide for deploying Evernote integrations to robust production environments across major cloud platforms (AWS, GCP, Kubernetes). It covers advanced practices including multi-stage Docker builds, secure secrets management via Secret Manager, serverless function setup (Lambda), and configuring necessary health checks and CI/CD pipelines. Ideal for transitioning from testing to live deployment.

This guide provides comprehensive best practices for securing Evernote API integrations. It covers essential topics like secure credential management using environment variables or vaults, implementing CSRF-protected OAuth flows, encrypting access tokens at rest using AES-256-GCM, rigorous input validation, and ensuring secure logging practices by redacting sensitive data. Use this when hardening your application against data breaches.

Automatically assists with generating Firebase security rules, offering best-practice guidance, template configurations, and validation checks tailored for GCP compute, storage, and data workloads.

This guide provides a comprehensive, multi-step technical process for migrating historical meeting recordings and data into the Fireflies.ai ecosystem. It covers various scenarios, including batch uploads from public URLs, importing authenticated files (S3/GCS), direct file uploads, and tracking progress via webhooks, ensuring a robust and validated transition from competitor platforms.

Automated Firestore index guidance for GCP Skills, triggering on index creator requests to deliver best-practice configs, validation, and production-ready code for Firebase/BigQuery/Vertex AI workloads.

Automates firewall rule generation within GCP Skills by responding when you mention firewall rule generator, delivering best-practice guidance, production-ready configs, and validation for compute, storage, BigQuery, Vertex AI, and related services.

A comprehensive guide for deploying applications, especially those integrated with AI services like Gamma, to major cloud environments including Vercel, AWS, GCP, and GitHub. Covers best practices for secret management, environment variable configuration, and setting up CI/CD pipelines across various cloud architectures.

Automates GC log analyzer workflows for performance testing by guiding load, stress, benchmarking, and monitoring efforts with best-practice advice and ready-to-use configurations.

Guides building production-ready serverless apps on GCP with Cloud Run services and Functions, covering containerized APIs, event-driven Pub/Sub workflows, cold-start tuning, and deployment automation.

Guides GCP users through GCS bucket configuration, offering best practices, configuration patterns, and production-ready snippets whenever someone asks about storage setup within Google Cloud.

Provides automated guidance for Google Cloud Storage lifecycle policy scenarios, including pattern recommendations, production-ready configurations, and best practices for compute, storage, BigQuery, Vertex AI, and other GCP-specific services.