ML Ops
Machine learning operations
nuxt-layers
by leeovery
Working with Nuxt layers (base, nuxt-ui, x-ui) that provide shared functionality. Use when understanding layer architecture, importing from layers, extending layer functionality, or creating new layers.
token-optimizer
by alexismunoz1
Practical guide to reduce token consumption, lower AI costs, and improve Claude Code performance through file organization, context management, and strategic model selection. Backed by real experiment data. Use when user mentions "optimize tokens", "reduce costs", "Claude is slow", "too many tokens", "token budget", "context window full", "organize codebase for AI", or "reduce token consumption". Do NOT use for general coding questions, debugging, or performance optimization unrelated to AI token usage.
ai-ethics
by 89jobrien
Responsible AI development and ethical considerations. Use when evaluating
discord-admin-py
by JungHoonGhae
Discord server administration via inference.sh - Multi-function app for channel, role, member management, messages, and more. Use for Discord bot operations, server management, channel creation, role assignment, and message handling.
image-upscale
by agntswrm
Upscales an image using AI super-resolution to increase resolution with detail generation. Use when you need to enlarge images, improve low-resolution photos, or prepare images for large-format display.
nuxt-models
by leeovery
Domain model classes with automatic hydration, relations, and type casting. Use when creating models for API entities, defining relationships between models, casting properties to enums/dates, or creating value objects.
machine-learning
by 89jobrien
Machine learning development patterns, model training, evaluation, and
skills
by atxinsky
"You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores requirements and design before implementation."
multi-model-research
by krishagel
Orchestrate multiple frontier LLMs (Claude, GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1) for comprehensive research using LLM Council pattern with peer review and synthesis
data-product-thinking
by hollandkevint
First-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for data offerings, or when someone asks "should we build this data product?"
ethical-risk-assessment
by hollandkevint
Ethical data risk evaluation, bias testing protocols, and governance practices for data products. Use when evaluating ML/AI features for fairness, designing bias testing protocols, planning phased rollouts for high-risk changes, reviewing data governance practices, or when someone asks "could this model be biased?" or "how do we ship AI features responsibly?" For HIPAA-specific guidance, see healthcare-data-domain.
reinforcement-learning
by Aznatkoiny
Reinforcement Learning best practices for Python using modern libraries (Stable-Baselines3, RLlib, Gymnasium). Use when: - Implementing RL algorithms (PPO, SAC, DQN, TD3, A2C) - Creating custom Gymnasium environments - Training, debugging, or evaluating RL agents - Setting up hyperparameter tuning for RL - Deploying RL models to production
openclaw-setup
by Aznatkoiny
Set up, install, configure, and deploy OpenClaw (formerly ClawdBot/MoltBot) — a personal AI assistant that runs on your own devices and connects to messaging channels. Use when users ask to "set up OpenClaw," "install ClawdBot," "install MoltBot," "deploy a personal AI assistant," "configure OpenClaw on Mac," "deploy OpenClaw to VPS," "set up OpenClaw on Hostinger," "connect OpenClaw to Telegram," "configure iMessage with OpenClaw," or any variation involving OpenClaw installation, gateway configuration, channel setup, Anthropic auth, or security hardening. Also triggers on "openclaw onboard," "openclaw doctor," "openclaw security audit," troubleshooting OpenClaw deployments, OpenClaw security, OpenClaw cost control, or ClawHub skills safety.
cpp-reinforcement-learning
by Aznatkoiny
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
gemini
by G1Joshi
Google Gemini AI models for multimodal tasks. Use for multimodal AI.
huggingface
by G1Joshi
Hugging Face transformers library and hub. Use for NLP models.
video-understand
by jrusso1020
Video understanding and transcription with intelligent multi-provider fallback. Use when: (1) Transcribing video or audio content, (2) Understanding video content including visual elements and scenes, (3) Analyzing YouTube videos by URL, (4) Extracting information from local video files, (5) Getting timestamps, summaries, or answering questions about video content. Automatically selects the best available provider based on configured API keys - prefers full video understanding (Gemini/OpenRouter) over ASR-only providers. Supports model selection per provider.
jupyter
by G1Joshi
Jupyter notebooks for interactive computing. Use for data exploration.
modelslab-model-discovery
by ModelsLab
Search and discover 50,000+ AI models on ModelsLab, check usage analytics, and monitor generation history via the Agent Control Plane API.
domain-driven-design
by G1Joshi
DDD tactical and strategic patterns. Use for complex domains.
pytorch
by G1Joshi
PyTorch deep learning framework with dynamic graphs. Use for neural networks.
opencv
by G1Joshi
OpenCV computer vision library. Use for image processing.
keras
by G1Joshi
Keras high-level neural network API. Use for deep learning.
debug:tensorflow
by SnakeO
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.