Use when a model won't learn, loss is NaN, metrics look too good/bad, or training is unstable. Provides a systematic decision tree for diagnosing data, optimization, and…
Prepares ML models for production deployment with containerization, API creation, monitoring setup, and A/B testing.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
Operate as an ML engineer who takes a research model to reliable production behind eval gates and keeps it healthy across its lifecycle.
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring. — from belokonm/claude-supercode-skills
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring. — from majiayu000/claude-skill-registry
Plan and execute production ML engineering work — model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, and…
Déploiement de modèles ML en production (MLOps). Se déclenche avec "déployer un modèle", "ML deployment", "MLOps", "model serving", "inference", "model registry", "ML pip — from…
Reference for evaluating machine learning models correctly — picking the right metric for the problem, detecting overfitting, and designing a validation strategy that gives an…
Expert MLOps engineering covering model deployment, ML pipelines, model monitoring, feature stores, and infrastructure automation.
Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins
Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates.
Orchestrates complete machine learning pipelines within SpecWeave increments. Activates when users request "ML pipeline", "train model", "build ML system", "end-to-end ML", "ML…
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. — from whatamelon/AGENT-HARNESS-BASELINE
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. — from majiayu000/claude-skill-registry
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Use when the user wants an implementation plan, architecture design, or multi-step ML pipeline — "build X", "implement X", "design X", "set up X
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring.
A Principal ML Engineer interviewer that simulates a FAANG-style ML system design interview covering the full lifecycle from data to production.
Use when designing end-to-end ML systems, choosing batch vs streaming inference, preventing training/serving skew, building data flywheels, or planning ML infrastructure scaling.
End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems.
Use when the user wants to verify code, config, or math before running — or proactively before any expensive training job or deployment
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback.
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
Comprehensive reference documentation and skill for MLIR (Multi-Level Intermediate Representation) - the extensible compiler infrastructure framework from the LLVM project.
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples. — from NVIDIA/skills
Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and…
Implement advanced MLOps practices for production ML systems. Use for: building CI/CD pipelines for ML models, implementing continuous training and monitoring, managing model…
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and…
Design DAG-based MLOps pipeline architectures with Airflow, Dagster, Kubeflow, or Prefect. Activates for DAG orchestration, workflow automation, pipeline design patterns, CI/CD…
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving.
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving.
MLX LM (mlx-lm) expert — Apple Silicon LLM inference with generate/chat/server commands, Python API, OpenAI-compatible HTTP server, and model conversion/quantization via…
Guides and validates architecture-aware ports of PyTorch/Hugging Face models to Apple MLX, inspects existing local MLX projects, and plans evidence-gated optimizations for Apple…
Audit Python MLX repos for lazy-eval, synchronization, compile, dtype, memory, progress, and benchmark issues.
MLX Swift - High-performance ML framework for Apple Silicon with lazy evaluation, automatic differentiation, and unified memory
MLX-VLM — Inference y fine-tuning de Vision Language Models en Mac usando MLX. Soporta LLaVA, Pixtral, Florence2, Molmo, Paligemma, IDEFICS y más.
MMB (Materal.MergeBlock) 项目 EF Core 迁移技能。用于在非 Visual Studio 环境（终端/Codex/CI）中添加、回滚与检查迁移，重点解决“VS 包管理控制台 Add-Migration 如何映射到 dotnet ef 命令”的问题。使用场景：(1) 用户提到…
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, al — from…
Practical workflows, context engineering strategies, and real-world execution examples for MoAI-ADK. Use when learning workflow patterns, optimizing context management, debugging…
Alfred SuperAgent의 필수 규칙을 정의합니다. November 2025 enterprise standard 기반. 3-Layer architecture, 4-Step workflow, Agent-first paradigm, Skill invocation rules, AskUserQuestio — from…
Alfred SuperAgent의 필수 규칙을 정의합니다. November 2025 enterprise standard 기반. 3-Layer architecture, 4-Step workflow, Agent-first paradigm, Skill invocation rules, AskUserQuestio — from…
Authoring CLAUDE.md Project Instructions. Design project-specific AI guidance, document workflows, define architecture patterns.
Backend development specialist covering API design, database integration, microservices architecture, and modern backend patterns.
Database specialist covering PostgreSQL, MongoDB, Redis, Oracle, and advanced data patterns for modern applications.
Frontend development specialist covering React 19, Next.js 16, Vue 3.5, and modern UI/UX patterns with component architecture.
Mobile native and cross-platform development domain skill covering iOS native (Swift/SwiftUI), Android native (Kotlin/Jetpack Compose), React Native, and Flutter.
Strategic thinking framework integrating First Principles Analysis, Stanford Design Thinking, and MIT Systems Engineering for deeper problem-solving.
Electron 33+ desktop app development specialist covering Main/Renderer process architecture, IPC communication, auto-update, and packaging with Electron Forge.
MoAI-ADK harness pattern library — unified domain knowledge covering hook/CI dispatch (PostToolUse, SessionStart, GitHub Actions, release automation), workflow patterns (SPEC…
MoAI super agent - unified orchestrator for autonomous development. Routes natural language or explicit subcommands (plan, run, sync, fix, loop, project, feedback) to spe — from…
MoAI super agent - unified orchestrator for autonomous development. Routes natural language or explicit subcommands (plan, run, sync, fix, loop, project, feedback) to spe — from…
PHP 8.3+ development specialist covering Laravel 11, Symfony 7, Eloquent ORM, and modern PHP patterns. Use when developing PHP APIs, web applications, or Laravel/Symfony projects.
Enterprise Mermaid diagramming skill for Claude Code using MCP Playwright. Use when creating architecture diagrams, flowcharts, sequence diagrams, or visual documentation.