GitHub
ai-engineer-expert
Expert-level AI implementation, deployment, LLM integration, and production AI systems
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tools machine learning AI Skills
Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
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GitHub
ml-expert
Expert-level machine learning, deep learning, model training, and MLOps
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GitHub
skill-reviewer
Evaluates skill effectiveness, suggests improvements to instructions, identifies missing edge cases, and recommends structure changes.
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GitHub
unsloth-training
Fine-tune LLMs with Unsloth using GRPO or SFT. Supports FP8, vision models, mobile deployment, Docker, packing, GGUF export. Use when: train with GRPO, fine-tune, reward functions, SFT training, FP8 training, v...
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GitHub
runpod-deployment
Deploy GPU workloads to RunPod serverless and pods - vLLM endpoints, A100/H100 setup, scale-to-zero, cost optimization. Use when: deploy to RunPod, GPU serverless, vLLM endpoint, scale to zero, A100 deployment,...
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GitHub
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt temp...
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GitHub
modelmanagement
Instructions for managing AI models and providers, including icon usage and implementation status.
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GitHub
coreml-optimizer
Optimize CoreML models for iOS and macOS deployment. Covers quantization, palettization, pruning, Neural Engine targeting, compute unit selection, and performance profiling. Use when converting ML models to Cor...
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GitHub
balls-mode
Decomposed reasoning with explicit confidence scoring. Use for complex decisions, debugging failures, and architectural choices where tracking uncertainty prevents wasted effort.
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GitHub
east-py-datascience
Data science and machine learning platform functions for the East language (TypeScript types). Use when writing East programs that need optimization (MADS, Optuna, SimAnneal, Scipy), machine learning (XGBoost,...
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GitHub
ai-data-analyst
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create...
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GitHub
senior-prompt-engineer
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-though...
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GitHub
airalogy-protocol
Expert guide for creating Airalogy Protocols (AIMD), Assigners (Python logic), and Validation Models. Use this to design digital workflows and experiments.
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GitHub
prompt-engineer
Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts.
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GitHub
hardhat
Use when creating an R modeling package that needs standardized preprocessing for formula, data frame, matrix, and recipe interfaces. Covers: mold() for training data preprocessing, forge() for prediction data...
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GitHub
tidymodels-overview
This skill should be used when working with R tidymodels packages, including when the user asks to "create a tidymodels workflow", "build a recipe", "tune a model", "use parsnip", "set up resampling", "create a...
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GitHub
workers-ai
This skill should be used when the user asks about "Workers AI", "AI models", "text generation", "embeddings", "semantic search", "RAG", "Retrieval Augmented Generation", "AI inference", "LLaMA", "Llama", "bge...
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GitHub
performing-eda
Conducts Exploratory Data Analysis (EDA) on datasets. Use when the user asks to "explore", "clean", or "visualize" a new CSV or dataset.
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GitHub
unsloth-finetuning
Fine-tune LLMs 2x faster with 80% less memory using Unsloth. Use when the user wants to fine-tune models like Llama, Mistral, Phi, or Gemma. Handles model loading, LoRA configuration, training, and model export...
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GitHub
superbpe
Train and use SuperBPE tokenizers for 20-33% token reduction across any project. Covers training, optimization, validation, and integration with any LLM framework. Use when you need efficient tokenization, want...
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GitHub
unsloth-tokenizer
Analyze, compare, and work with tokenizers using Unsloth tools. Compare different tokenizers, analyze token efficiency, and integrate with Unsloth models. For SuperBPE training, see the 'superbpe' skill.
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GitHub
training-optimization
Advanced techniques for optimizing LLM fine-tuning. Covers learning rates, LoRA configuration, batch sizes, gradient strategies, hyperparameter tuning, and monitoring. Use when fine-tuning models for best perfo...
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GitHub
model-deployment
Export and deploy fine-tuned models to production. Covers GGUF/Ollama, vLLM, HuggingFace Hub, Docker, quantization, and platform selection. Use after fine-tuning when you need to deploy models efficiently.
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GitHub
adaptive-workflows
Self-learning workflow system that tracks what works best for your use cases. Records experiment results, suggests optimizations, creates custom templates, and builds a personal knowledge base. Use to learn fro...
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