Skill UI

Expert guidance for fine-tuning LLMs with Axolotl, covering YAML configs, LoRA/QLoRA/DPO variants, multimodal support, and practical troubleshooting patterns for development and training workflows.

Expert guidance for fine-tuning LLMs with Axolotl, covering YAML configs, LoRA/QLoRA/DPO variants, multimodal support, and practical troubleshooting patterns for development and training workflows.

This skill provides access to official Anthropic brand identity resources, ensuring visual consistency across all documents. It automatically applies defined brand colors (primary, accent, neutral) and typography (Poppins for headings, Lora for body text) while maintaining structural integrity and readability. Ideal for professional reports, presentations, and marketing materials needing strict corporate adherence.

This skill provides access to Anthropic's official brand identity and style resources. It ensures consistent and professional visual branding by managing defined primary and accent color palettes, and applying specific typography rules (e.g., Poppins for headings, Lora for body text). It guarantees that the output maintains a cohesive, on-brand appearance across various systems.

Guides researchers through ten structured ideation frameworks to move from vague curiosity to actionable, defensible research proposals, supporting exploration of new problem spaces, pivoting projects, and surfacing high-impact angles.

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

Guides ML engineers through dataset prep, PEFT strategy selection, LoRA/QLoRA adapter setup, training monitoring, and deployment checks for practical LLM fine-tuning, quantization, and adapter merge workflows.

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

Automates HelpDesk ticket browsing, view inspection, canned responses, and custom-field exploration via Composio’s Rube MCP toolkit, urging an initial RUBE_SEARCH_TOOLS call so you always work with the latest schemas before executing workflows.

Provides calibration-free weight quantization for LLMs down to 1-bit precision with multiple optimized backends, ideal for fast deployments with vLLM/HuggingFace and LoRA/PEFT fine-tuning without collecting calibration data.

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.