Skill UI

This skill provides advanced integration for deploying applications on Fly.io, focusing on edge computing architectures. It covers building production-ready Docker images, configuring `fly.toml` for services and health checks, and executing sophisticated deployment strategies like blue-green and canary releases. It ensures reliable, scalable deployment across multiple regions, including automated rollbacks and micro-VM optimization.

This comprehensive guide provides essential security best practices for deploying applications on Fly.io. It covers critical areas such as secrets management, private networking using WireGuard, securing TLS certificates, and properly scoping deploy tokens. The included functions demonstrate how to validate API tokens, verify webhook signatures, and validate deployment configurations to mitigate common cloud and application-level vulnerabilities.

This guide provides methods for robust monitoring and automation tailored for services deployed on Fly.io. It covers key areas including polling the Machines API to detect state changes, implementing comprehensive health check endpoints, processing structured logs using tools like `jq`, and automating deployment notifications within CI/CD pipelines. Essential for achieving high availability and reliable operations.

This guide provides comprehensive strategies to optimize financial workflows within the Fondo system. Key areas covered include accelerating the month-end close process through auto-categorization rule setup, streamlining vendor communications with CPAs via batch processing, and implementing efficient data export caching mechanisms. It is designed to help finance teams achieve faster, more accurate, and highly automated financial reporting.

A comprehensive guide to production-ready, robust coding patterns for Framer development. This resource covers key areas including type-safe CMS interactions, plugin state management hooks, reusable component architectures, and server API wrappers. Use this when refactoring existing SDK usage or establishing scalable coding standards for complex Framer integrations.

This guide provides comprehensive security best practices for developers building with Framer. It covers secure credential management (like API keys and Site IDs), implementing the principle of least privilege, and securing plugins and Server API connections. Use this when auditing your Framer setup or ensuring production-level security for sensitive data handling.

A comprehensive guide designed to diagnose and resolve common technical errors encountered when integrating with the Glean enterprise search API. It covers critical areas such as token type mismatches, indexing pipeline failures, permission restrictions, and rate limiting, providing actionable fixes for maintaining seamless search functionality.

This skill acts as an intensive sparring partner, rigorously questioning any provided plan, design, or concept. It guides the user through every decision branch, resolving dependencies one by one until a comprehensive and shared understanding is achieved. Ideal for stress-testing ideas, identifying blind spots, and ensuring the robustness of complex projects before implementation.

This guide provides comprehensive solutions for diagnosing and resolving common errors and exceptions encountered while using the Hex data platform. It covers critical areas such as API authentication failures (401, 403), resource not found issues (404), rate limiting, and various run status errors (ERRORED, KILLED). This is essential for debugging failed data requests, optimizing integration workflows, and ensuring stable data analytics pipelines.

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

This bridge provides bidirectional integration between Home Assistant (HA) and advanced AI pipelines. It streams HA camera feeds into the AI system for sophisticated analysis (detection, VLM, REID). Conversely, the analysis results—such as detected objects, descriptions, and person identities—are sent back to HA as image_processing entities, enabling powerful automation and smart notifications.

This benchmark evaluates Vision-Language Models (VLMs) on detecting potential safety hazards within indoor environments. It uses static camera frames, simulating real-world fixed security camera monitoring. The test covers 40 scenarios across 5 critical categories, including fire/smoke, electrical risks, trips/falls, child safety, and falling objects, providing a rigorous measure of VLM's practical safety inspection capability.