chatkit-store-creator
This skill helps create ChatKit store implementations with database integration, following best practices and avoiding common errors we've solved in previous implementations.
This skill helps create ChatKit store implementations with database integration, following best practices and avoiding common errors we've solved in previous implementations.
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
Generate audio from text using 13 TTS providers (local + cloud). Use when user wants to create audio files, convert text to speech, generate voiceovers, create audio with different voices, use voice cloning, multilingual TTS, or mentions /create-audio command. Supports Pocket TTS (CPU, 8 voices), MLX-Audio (Apple Silicon, 7 models, 50+ voices), ElevenLabs (cloud API, 32 languages, 10k+ voices), and Coqui TTS (open source, 4 models, voice cloning). Includes 32+ languages, voice cloning, speed control, and both local and cloud options.
Current state of Claude's memory, context, and Skills architecture as of late 2025. LOAD THIS when discussing context engineering, Skills, userMemories, memory management, context portability across surfaces, or troubleshooting why context isn't loading. Contains experimental findings and observed behaviors NOT in training data.
Use when diagnosing openai_harmony.HarmonyError or gpt-oss tool calling issues with vLLM. Identifies error sources (vLLM server vs client), maps specific error messages to known GitHub issues, and provides configuration fixes for tool calling problems with gpt-oss models.
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
Model fine-tuning with PyTorch and HuggingFace Trainer. Covers dataset preparation, tokenization, training loops, TrainingArguments, SFTTrainer for instruction tuning, evaluation, and checkpoint management. Includes Unsloth recommendations.
Embedding model configurations and cost calculators
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
Production-grade data science specialist with TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 expertise. Master data processing, ML pipeline development, model deployment, and statistical analysis. Build end-to-end data science solutions with comprehensive experimentation and visualization.
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 workflow_set", "compare models", "stack models", or mentions tidymodels packages like recipes, parsnip, workflows, workflowsets, tune, rsample, yardstick, or stacks. Provides ecosystem context before package-specific skills.
Instrument evaluation metrics, quality scores, and feedback loops
Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.
Cross-validation configuration and fold management for this competition
Guide for designing Instance resources in OptAIC. Use when creating DatasetInstance, SignalInstance, ExperimentInstance, ModelInstance, PortfolioOptimizerInstance, or BacktestInstance. Covers definition references, config patterns, composition, flow execution pairing, and scheduling.
Performing full fine-tuning (FFT) in Unsloth with 100% exact weight updates and optimized gradient checkpointing. Triggers include fft, full fine-tuning, full_finetuning, exact fine-tuning, and weight updates.
Enterprise Machine Learning specialist with TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 expertise. Master AutoML, neural architecture search, MLOps automation, and production ML deployment. Build scalable ML pipelines with comprehensive monitoring and experiment tracking.
Use when "scikit-learn", "sklearn", "machine learning", "classification", "regression", "clustering", or asking about "train test split", "cross validation", "hyperparameter tuning", "ML pipeline", "random forest", "SVM", "preprocessing"
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus