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
Independently audit ML release and reproducibility claims when a user asks whether a model, dataset, run, artifact, or deployment is ready; reconcile provenance, hashes, privacy,…
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…
Operate the MMR algorithmic trading platform on Interactive Brokers. Trade stocks and options, manage portfolios, scan for ideas, create trade proposals with auto position sizing,…
Source-cited draft: company formation & entity choice for Mongolia (tax year 2025) — rates, thresholds and rules with primary-source citations.
Source-cited draft: corporate income tax for Mongolia (tax year 2025) — rates, thresholds and rules with primary-source citations. Unverified; pending local-accountant review.
Source-cited draft: payroll & social contributions for Mongolia (tax year 2025) — rates, thresholds and rules with primary-source citations.
Source-cited draft: tax overview for Mongolia (tax year 2025) — rates, thresholds and rules with primary-source citations. Unverified; pending local-accountant review.
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.
앱인토스(Apps in Toss) 미니앱 개발 전문가. WebView/React Native SDK, 토스 로그인/결제, 푸시 알림, 광고 통합을 다룹니다. 토스 앱 내 미니앱 개발, Granite 프레임워크 사용, 토스페이 결제 연동 시 사용하세요. — from toss/apps-in-toss-examples
앱인토스(Apps in Toss) 미니앱 개발 전문가. WebView/React Native SDK, 토스 로그인/결제, 푸시 알림, 광고 통합을 다룹니다. 토스 앱 내 미니앱 개발, Granite 프레임워크 사용, 토스페이 결제 연동 시 사용하세요. — from majiayu000/claude-skill-registry
Authentication and authorization specialist covering Auth0, Clerk, and Firebase Auth. Use when implementing authentication, MFA, SSO, passkeys, WebAuthn, social login, or security…
Chrome Extension Manifest V3 development specialist covering service workers, content scripts, message passing, chrome.* APIs, side panel, declarativeNetRequest, and Chrome Web…
Cloud database platform specialist covering Neon (serverless PostgreSQL), Supabase (PostgreSQL 16 with real-time), and Firebase Firestore (NoSQL with offline sync).
Deployment and hosting platform specialist covering Vercel, Railway, and Convex. Use when deploying applications, configuring edge functions, setting up continuous deployment, or…
Git workflow patterns, branch strategies, conventional commits, and PR templates reference for git operations.
React/Next.js component design patterns, state management strategies, and project structure reference for frontend development.
Test pyramid strategy, coverage targets, test patterns, and quality metrics reference. Agent-extending skill that amplifies manager-develop and manager-quality expertise with…