ai-risk-assessment
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
Meta-skill that teaches how to design Claude Code skills following the Box Factory philosophy. Helps you understand when to create skills, how to structure them for low maintenance, and how to add value beyond documentation. Use when creating or reviewing skills.
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.
Fetches up-to-date documentation for technologies used in the Personal AI Employee project via Context7 MCP server. Use when you need current API documentation, library references, code examples, or implementation guidance for any technology in the stack.
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.
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.
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.
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
Rigorous RL evaluation - statistical protocols, train/test discipline, metrics, generalization
Create, validate, and debug YAML training configurations for axolotl-rs fine-tuning
See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
WHEN: Machine Learning/Deep Learning code review, PyTorch/TensorFlow patterns, Model training optimization, MLOps checks WHAT: Model architecture review + Training patterns + Data pipeline checks + GPU optimization + Experiment tracking WHEN NOT: Data analysis only → python-data-reviewer, General Python → python-reviewer