Skill UI

This guide details the use of the GGUF format and quantization techniques for running large language models (LLMs) efficiently. GGUF enables optimal inference on consumer hardware, Apple Silicon, and systems requiring CPU-only operation. By compressing models using various K-quant methods (like Q4_K_M), developers can significantly reduce memory footprint and hardware requirements while maintaining high performance.

A comprehensive collection of patterns and best practices for automating GitHub workflows using advanced AI models. This skill enables automated PR reviews, sophisticated issue triage (labeling and commenting), and seamless integration of AI into CI/CD pipelines, significantly enhancing DevOps efficiency and code quality assurance.

This comprehensive workflow automates the entire post-meeting cycle. It enhances raw transcripts by structuring notes, extracting action items, and summarizing key decisions using advanced AI models. Users can then seamlessly share these structured notes to integrations like Slack, Notion, and CRMs (HubSpot), while also using the Chat feature to draft follow-up emails and automatically update contact records in the built-in CRM.

Configure robust CI/CD pipelines for Groq API integrations using GitHub Actions. This guide covers setting up unit tests (mocked), live integration tests, and automated model deprecation checks. It ensures that your LLM-powered application remains stable and compatible as models evolve.

A comprehensive workflow solution for Groq, enabling powerful multimodal tasks beyond simple chat completions. It supports ultra-fast audio transcription (Whisper), detailed image understanding using vision models, and professional text-to-speech generation. Use this for real-time content processing, multimedia analysis, and automated content creation.

A comprehensive guide to optimizing Groq API usage and managing inference costs. Learn techniques such as smart model routing (selecting smaller models for simple tasks), minimizing prompt tokens, batching requests to reduce overhead, and implementing caching for deterministic calls. This guide helps developers achieve significant cost savings and improve system efficiency when handling high-volume AI workloads.

The Hugging Face Hub CLI (`hf`) is a comprehensive command-line tool for managing the entire lifecycle of AI assets. It allows users to download models, upload datasets, synchronize files to buckets, manage authentication tokens, and interact with collections and discussions. This tool is essential for automating MLOps workflows and managing resources directly from the terminal.

This skill provides comprehensive methods to significantly reduce expenditure when using the Ideogram API for image generation. Techniques include implementing two-phase generation workflows (drafting with cheap models, finalizing with premium models), batching multiple images in a single API call, caching identical prompts, and setting up robust budget tracking alerts. Ideal for developers managing large-scale creative AI projects.

A comprehensive guide for configuring API access across development, staging, and production environments. This setup ensures strict isolation of API keys and environment-specific parameters (like models, speed, and caching). It covers best practices for secret management using major CI/CD and secret services (GitHub Actions, AWS Secrets Manager, GCP Secret Manager).

LitGPT provides clean, readable, and production-ready implementations for over 20 state-of-the-art LLMs (including Llama, Gemma, Phi, Mistral). It supports comprehensive workflows, including full fine-tuning, efficient LoRA/QLoRA adaptation, and pretraining from scratch. Ideal for developers needing academic understanding or robust, deployable model architectures.

Instructor lets you shape LLM responses into validated Pydantic models, automatically retrying failed extractions, parsing complex JSON with type safety, and streaming partial outputs across Anthropic, OpenAI, and other providers.

This skill serves as a comprehensive reference guide for Kling AI's various generative models. It details capabilities across video generation (T2V, I2V), image styling, lip sync, and virtual try-on. Users can compare model versions based on quality, speed, and cost to select the optimal model for their creative or production pipeline.