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By ananddtyagi
Rapidly implement production-ready AI/ML features in apps: integrate LLMs via prompt engineering and response handling, build ML pipelines for user behavior-based recommendations, add computer vision for photo-based product search, and deploy intelligent automations.
npx claudepluginhub ananddtyagi/claude-code-marketplace --plugin ai-engineerCommunity-driven marketplace for Claude Code commands and plugins.
Add this marketplace to Claude Code:
/plugin marketplace add ananddtyagi/cc-marketplace
Then browse and install individual plugins (commands or agents):
/plugin
Install a specific command:
/plugin install lyra@cc-marketplace
Install a specific agent:
/plugin install accessibility-expert@cc-marketplace
Visit claudecodecommands.directory to:
Each plugin in this marketplace is independently installable:
/lyra, /audit, /ultrathink)Install only what you need - no bloat, full granular control!
This marketplace is automatically synced from the live database whenever commands are published or updated.
Individual commands may have their own licenses. See each command file for details.
Submit commands at claudecodecommands.directory/submit Submit agents at claudecodecommands.directory/submit-agent
Built with ❤️ by the Claude Code community
Uses power tools
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Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>
Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>
Use this agent when you need to design scalable architecture and folder structures for new features or projects. Examples include: when starting a new feature module, refactoring existing code organization, planning microservice boundaries, designing component hierarchies, or establishing project structure conventions. For example: user: 'I need to add a user authentication system to my app' -> assistant: 'I'll use the code-architect agent to design the architecture and folder structure for your authentication system' -> <uses agent>. Another example: user: 'How should I organize my e-commerce product catalog feature?' -> assistant: 'Let me use the code-architect agent to design a scalable structure for your product catalog' -> <uses agent>.
Agents for data engineering, machine learning, and AI development
Production-ready Claude Code configuration with role-based workflows (PM→Lead→Designer→Dev→QA), safety hooks, 44 commands, 19 skills, 8 agents, 43 rules, 30 hook scripts across 19 events, auto-learning pipeline, hook profiles, and multi-language coding standards
Pydantic AI, LangGraph, DeepAgents, and Vercel AI SDK skills for building and reviewing AI applications.
Model Context Protocol Mcp Expert subagent
Generate API documentation for endpoints
Use this agent when planning 6-day development cycles, prioritizing features, managing product roadmaps, or making trade-off decisions. This agent specializes in maximizing value delivery within tight timelines. Examples:\n\n<example>\nContext: Planning the next sprint\nuser: "We have 50 feature requests but only 6 days"\nassistant: "I'll help prioritize for maximum impact. Let me use the sprint-prioritizer agent to create a focused sprint plan that delivers the most value."\n<commentary>\nSprint planning requires balancing user needs, technical constraints, and business goals.\n</commentary>\n</example>\n\n<example>\nContext: Making feature trade-offs\nuser: "Should we build AI chat or improve onboarding?"\nassistant: "Let's analyze the impact of each option. I'll use the sprint-prioritizer agent to evaluate ROI and make a data-driven recommendation."\n<commentary>\nFeature prioritization requires analyzing user impact, development effort, and strategic alignment.\n</commentary>\n</example>\n\n<example>\nContext: Mid-sprint scope changes\nuser: "The CEO wants us to add video calling to this sprint"\nassistant: "I'll assess the impact on current commitments. Let me use the sprint-prioritizer agent to reorganize priorities while maintaining sprint goals."\n<commentary>\nScope changes require careful rebalancing to avoid sprint failure.\n</commentary>\n</example>
Use this agent when you need to implement comprehensive monitoring, observability, and alerting systems for enterprise B2B applications. This agent specializes in APM, logging, metrics, distributed tracing, SLA monitoring, and proactive incident management for business-critical systems. Examples:
Use this agent when you need to optimize customer success operations for B2B enterprise clients. This agent specializes in customer health monitoring, expansion revenue identification, churn prevention, enterprise account management, and customer lifecycle optimization. Handles enterprise onboarding, adoption tracking, and strategic account growth. Examples:
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