GitHub
ml-systems
Machine Learning Systems - comprehensive knowledge for building production ML systems from data engineering through deployment and operations. Based on Harvard ML Systems course and Designing ML Systems by Chip...
Skills IA tools machine learning
Découvrez les skills tools machine learning, comparez leurs dépôts et consultez leurs instructions.
Catégories: tools machine learning
tools machine learning Effacer les filtres
Explorer
GitHub
training-data
Training data management including labeling strategies, data augmentation, handling imbalanced data, and data splitting best practices.
GitHub
deep-learning-primer
Deep learning foundations including neural network basics, backpropagation, optimization, regularization, and training best practices.
GitHub
robust-ai
Building robust AI systems including model monitoring, drift detection, reliability engineering, and failure handling for production ML.
GitHub
machine-learning
Python machine learning with scikit-learn, PyTorch, and TensorFlow
GitHub
production-eval-strategy
Strategies for evaluating agents in production - sampling, baselines, and regression detection
GitHub
evaluation-quality
Instrument evaluation metrics, quality scores, and feedback loops
GitHub
defense-in-depth
Multi-layer validation to catch bugs before they escape
GitHub
development-memory
Build persistent project knowledge using checkpoint/recall. Activates when fixing bugs, making decisions, or investigating past work. Creates automatic knowledge base through systematic checkpointing and semant...
GitHub
learning-taxonomy
Expert knowledge for categorizing learnings and routing them to appropriate improvement targets. Use when analyzing session learnings to determine where context improvements should be applied.
GitHub
moai-learning-optimizer
Intelligent learning system optimizer that analyzes session patterns, identifies improvement opportunities, and continuously enhances Alfred's performance through adaptive learning and knowledge optimization. U...
GitHub
moai-domain-data-science
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...
GitHub
moai-domain-ml
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 sca...
GitHub
moai-essentials-perf
AI-powered enterprise performance optimization orchestrator with Context7 integration, Scalene AI profiling, intelligent bottleneck detection, automated optimization strategies, and predictive performance tunin...
GitHub
trend-modeling
This skill should be used when the user asks to "model trends with limited data", "three-valued logic analysis", "scenario generation", "transitional graphs", "qualitative trend analysis", "uncertain data analy...
GitHub
requirements-translator
The Entry Point and Air Traffic Controller for the PM Brain. Transforms chaotic input (hashtags, raw text, voice notes) into structured, routed artifacts. Use for ambiguous input,
GitHub
ai-engineer
You are a highly skilled AI Engineer specializing in the practical application of machine learning models. You are an expert in Python and popular AI/ML frameworks like TensorFlow, PyTorch, and scikit-learn. Yo...
GitHub
section-ml-ai
生成机器学习与 AI 类页面的超详细教程内容，强调框架选择与实践流程；当需要撰写机器学习框架、算法或深度学习教程时使用
GitHub
cost-optimization
Manage Claude Code API costs - token strategies, model selection, monitoring. Use when concerned about API spend, optimizing token usage, choosing models for tasks, or setting up cost monitoring. Covers /cost c...
GitHub
behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
GitHub
adversarial-machine-learning
Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
GitHub
breadth-of-thought
Exhaustive solution space exploration methodology. Use when solution space is unknown, you need multiple viable options (not just one best), or can't afford to miss alternatives. Explores 8-10 approaches in par...
GitHub
benchmark-framework
Rigorous A/B/C testing framework for empirically evaluating reasoning patterns. Use when you need data-driven pattern selection, want to quantify trade-offs between patterns, or need to validate claims about wh...
GitHub
tinker-training-cost
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the corr...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.