Skill UI

Build, deploy, and maintain machine-learning applications on Hugging Face Spaces using Gradio, Docker, or Static SDKs, with ZeroGPU and dedicated hardware, model loading, debugging, and inference providers.

This skill enables users to build reusable command-line scripts and utilities for interacting with the Hugging Face API. It supports chaining API calls, automating repetitive tasks, and processing model or dataset data efficiently. Scripts utilize HF_TOKEN for authentication and can leverage the hf CLI for advanced repository interactions.

This skill focuses on identifying security vulnerabilities in Large Language Model (LLM) and AI features. It covers prompt injection, system prompt leakage, cross-tenant access, and data exfiltration techniques. Users must verify findings against false positives using reproducibility checks and out-of-band callbacks before reporting.

This intelligent studio acts as a visual creative director, automatically routing image requests to the optimal specialized AI model. It handles hyper-realistic portrait generation (for influencers/professional photos) and advanced artistic creation, illustration, and photo manipulation (upscale, inpainting, background removal). Use it for professional content creation requiring specialized visual expertise.

This skill leverages Google Gemini's advanced image generation model to create high-quality, context-specific visuals. It is essential for developers needing placeholder images for UIs, generating detailed diagrams for technical documentation, or visualizing abstract concepts, logos, and assets across cross-platform projects (Windows, macOS, Linux).

This skill reviews Bitcoin Lightning Network protocol designs and architecture. It compares channel factory approaches and analyzes Layer 2 scaling tradeoffs. It covers trust models, on-chain footprint, consensus requirements, HTLC/PTLC compatibility, liveness guarantees, and watchtower support. Suitable for expert evaluation of Lightning protocol improvements and security mechanisms.

Expert prompt engineering skill that transforms basic instructions into production-ready LLM prompts using constitutional AI, chain-of-thought reasoning and model-specific optimization to boost accuracy and cut token costs.

This skill helps reduce LLM API and infrastructure costs by 50-90% using strategies like model selection, prompt caching, batching, quantization, and self-hosting. It covers cost tracking, right-sizing models for tasks, provider-side caching, and semantic caching to avoid redundant calls. Suitable for developers managing AI spend and optimizing cloud infrastructure.

Run Fireworks-hosted open-weight model councils that compare responses and synthesize a final answer. This skill implements Karpathy's LLM Council concept with parallel model responses, ranking, and chairman synthesis.

This skill covers setting up infrastructure for fine-tuning Large Language Models (LLMs) using techniques like QLoRA, LoRA, and full fine-tuning. It includes configuring distributed training with DeepSpeed or FSDP, utilizing frameworks such as Hugging Face TRL and Axolotl, and implementing alignment methods like RLHF and DPO. Suitable for domain-specific model adaptation and production deployment.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.