Skill UI

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.

Automate the entire deployment lifecycle for ML inference services on CoreWeave GPU cloud. This workflow integrates unit testing, container image building (to GHCR), and rolling deployment to CoreWeave Kubernetes clusters via GitHub Actions. Ideal for ensuring stable, high-performance deployment of GPU-dependent models. Key features include validating GPU manifest requests and managing deployments using standard Kubernetes APIs.

A comprehensive tool for updating Feishu cloud documents. It supports seven distinct update modes including appending, full overwrite, targeted range replacement, and multi-instance replacement. The system allows highly precise positioning using content ranges (ellipsis) or chapter titles, ensuring content updates are localized and safe, thus preserving images, comments, and formatting.

This skill provides a comprehensive workflow to export images, icons, and assets directly from Figma files using the REST API. It enables users to render specific frames or components as high-quality PNG, SVG, JPG, or PDF files. Ideal for building automated asset pipelines, generating documentation previews, or batch exporting large icon libraries.

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.

This guide provides advanced techniques to boost Framer API performance. It covers critical optimization areas including batch CMS syncing to prevent timeouts, memoizing expensive React components for better render performance, establishing persistent WebSocket connections, and optimizing image URLs before CMS synchronization. Use this when aiming to improve overall speed and stability of Framer-integrated applications.

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.

Execute a comprehensive creative workflow utilizing the Adobe Firefly v3 API. This tool supports advanced AI image generation from text prompts (text-to-image), generative fill for inpainting masked areas, and image expansion (outpainting) to grow canvas size. Ideal for automating marketing asset creation and building complex creative pipelines.

Brandkit is an image generation skill that has been moved to a dedicated repository. It runs only when explicitly called by name. Users can install it using the npx skills add command from the Leonxlnx/imagegen-skills GitHub repository. This tool is part of a suite of image-generation skills designed for creative workflows.

This comprehensive workflow manages the entire Canva creative pipeline. Users can upload, read, and modify design assets (images, files) via API. Key features include autofilling brand templates using dynamic data (text, images, charts) and programmatically organizing generated designs into specific folders, significantly streamlining the content creation process.

This skill provides comprehensive guidance for deploying and managing GPU-accelerated AI inference services on CoreWeave Kubernetes (CKS). It covers best practices including containerization using NVIDIA CUDA base images, configuring specific GPU resource limits (A100/H100), setting up robust health checks, and executing controlled rolling updates. Ideal for managing multi-model inference and scaling demanding AI workloads in a cloud environment.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.