Skill UI

A quick reference guide for diagnosing and resolving common runtime and deployment errors encountered on the Fly.io platform. Covers issues such as health check failures, image build failures, machine startup issues, and connection refusals, providing specific diagnostic commands and configuration fixes.

A comprehensive guide to installing the flyctl CLI and setting up secure authentication for Fly.io edge compute deployments. This guide covers both interactive login and API token methods (User, Deploy, Org, Machines), which are essential for CI/CD pipelines and advanced resource management via the Machines API.

Outlines a comprehensive, fast local development loop for applications hosted on Fly.io. It guides users through testing Docker containers locally, proxying remote services like Postgres and Redis to localhost, configuring dev environments, and performing fast deployment cycles. Essential for modern edge computing development.

Provides a comprehensive reference architecture for deploying highly scalable, low-latency applications on Fly.io. It details best practices for multi-region web tiers, achieving high availability with Postgres read replicas, implementing Redis caching, managing background workers, and securing connectivity via private internal networking for robust production systems.

This skill creates and synchronizes prompt-based AI agents directly into Azure AI Foundry using the Agent Service REST API. By defining agents in a local JSON manifest, the skill registers them server-side, making them instantly available for evaluation and invocation within the Foundry ecosystem. It is essential for deploying, updating, or managing AI agents at scale within the Azure cloud environment.

A comprehensive checklist designed to guide users through deploying Framer integrations to a live production environment. It covers best practices for plugins, code components, server API synchronization (CMS), and final site publishing, ensuring a thorough and reliable go-live process.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

This skill provides a complete solution for deploying Glean custom connectors as scheduled jobs on container platforms (e.g., Cloud Run). It covers building a production-ready Docker image to manage document ingestion, custom data source connectivity, and search query execution. Includes robust health checks and rolling update strategies to ensure continuous, high-availability indexing for enterprise search environments.

A comprehensive checklist designed to validate the deployment, robustness, and security of an enterprise search system powered by Glean. It covers critical areas such as API integration, secrets management, error handling, monitoring, and permissions, ensuring a stable and reliable user experience in a production setting.

Skills this deployment guide covers building, configuring, and deploying a containerized Grammarly writing assistant service. It provides detailed instructions for production setup, including Docker configuration, environment variable management (for OAuth credentials), health checks, and zero-downtime rolling updates when integrating Grammarly's Text API for grammar, clarity, and tone checking.

This comprehensive checklist guides developers through the necessary steps to deploy a robust and reliable Grammarly API integration in a production environment. It covers critical aspects such as secure credential storage, rate limit handling, implementing text chunking, designing error resilience patterns (circuit breakers, retries), and establishing comprehensive monitoring and alerting systems to ensure high content quality and system uptime.

This guide provides techniques to significantly boost performance when integrating with the Hex API. It covers essential optimization strategies such as implementing LRU caching for project lists, running multiple independent jobs in parallel, and adopting adaptive polling intervals to manage API latency and resource usage effectively. Use this when facing slow data retrieval or requiring high-throughput data processing.