ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — content feeds, search ranking, RAG rerankers, task prioritizers, notification triage, ad selection.
Uses power tools
Uses Bash, Write, or Edit tools
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
⚡ Updated for Opus 4.7, Sonnet 4.6 & Haiku 4.5 — Three-tier model strategy for optimal performance
🎯 Agent Skills Enabled — 153 specialized skills extend Claude's capabilities across plugins with progressive disclosure
A comprehensive production-ready system combining 185 specialized AI agents, 16 multi-agent workflow orchestrators, 153 agent skills, and 100 commands organized into 80 focused, single-purpose plugins for Claude Code.
[!NOTE] Gemini CLI users: This ecosystem is also available as a native Gemini CLI extension — 153 skills discoverable on-demand, no plugin installation required. See GEMINI.md for setup.
This unified repository provides everything needed for intelligent automation and multi-agent orchestration across modern software development:
Each plugin is completely isolated with its own agents, commands, and skills:
Example: Installing python-development loads 3 Python agents, 1 scaffolding tool, and makes 16 skills available (~1000 tokens), not the entire marketplace.
Add this marketplace to Claude Code:
/plugin marketplace add wshobson/agents
This makes all 80 plugins available for installation, but does not load any agents or tools into your context.
Browse available plugins:
/plugin
Install the plugins you need:
# Essential development plugins
/plugin install python-development # Python with 16 specialized skills
/plugin install javascript-typescript # JS/TS with 4 specialized skills
/plugin install backend-development # Backend APIs with 3 architecture skills
# Infrastructure & operations
/plugin install kubernetes-operations # K8s with 4 deployment skills
/plugin install cloud-infrastructure # AWS/Azure/GCP with 4 cloud skills
# Security & quality
/plugin install security-scanning # SAST with security skill
/plugin install comprehensive-review # Multi-perspective code analysis
# Full-stack orchestration
/plugin install full-stack-orchestration # Multi-agent workflows
Each installed plugin loads only its specific agents, commands, and skills into Claude's context.
You install plugins, which bundle agents:
| Plugin | Agents |
|---|---|
comprehensive-review | architect-review, code-reviewer, security-auditor |
javascript-typescript | javascript-pro, typescript-pro |
python-development | python-pro, django-pro, fastapi-pro |
blockchain-web3 | blockchain-developer |
# ❌ Wrong - can't install agents directly
/plugin install typescript-pro
npx claudepluginhub p/csteffen-normalis-machine-learning-ops-plugins-machine-learning-opsCloud architecture design for AWS/Azure/GCP/OCI, Kubernetes cluster configuration, Terraform infrastructure-as-code, hybrid cloud networking, and multi-cloud cost optimization
Customer support workflow automation, sales pipeline management, email campaigns, and CRM integration
End-to-end feature orchestration with testing, security, performance, and deployment
Error analysis, trace debugging, and multi-agent problem diagnosis
Semantic anchor skills for translating established methodologies and installing persistent coding-agent context.
Harness-native ECC operator layer - 67 agents, 278 skills, 94 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.