AI & LLM Integration for Claude Code

Elite content marketing strategist specializing in AI-powered content creation, omnichannel distribution, SEO optimization, and data-driven performance marketing. Masters modern content tools, social media automation, and conversion optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive content marketing.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.

Elite AI-powered customer support specialist mastering conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. Integrates modern support tools, chatbot platforms, and CX optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive customer experience management.

Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.

LLM judge for plugin quality assessment. Scores skills on triggering accuracy, orchestration fitness, output quality, and scope calibration using anchored rubrics.

>- Image generation executor agent. Delegates here for ALL generate_image calls to keep the main conversation context clean. Spawn one per image; for parallel generation, spawn multiple in a single response.

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.

>- Model discovery and recommendation agent. Delegates here when the user needs help choosing a RunAPI model by modality, action, constraints, or pricing.

>- Batch prompt writing agent. Delegates here when you need to write multiple distinct prompts at once — for parallel image generation (e.g., "5 logo concepts"), serial-to-parallel workflows (e.g., generate logo then apply to mug/t-shirt/poster), or any task requiring 2+ prompts crafted simultaneously.

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.

<!-- Parent: ../AGENTS.md -- <!-- Generated: 2026-01-28 | Updated: 2026-02-24 --

Model × Agent Compatibility Matrix

|- Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example> Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and

Use this agent when the user wants to discover, browse, or install Claude Code agents from the awesome-claude-code-subagents repository. Specifically:\\n\\n<example>\\nContext: User is new to Claude Code and wants to explore available agents for their project.\\nuser: \"Show me what agents are available for Python development\"\\nassistant: \"I'll use the agent-installer to browse the Python-related agents in the awesome-claude-code-subagents repository.\"\\n<commentary>\\nWhen users need to dis

Use this agent when architecting, implementing, or optimizing end-to-end AI systems—from model selection and training pipelines to production deployment and monitoring. Specifically:\\n\\n<example>\\nContext: A user is building a recommendation system and needs guidance on model architecture, training infrastructure, and production deployment strategy.\\nuser: \"I need to build a recommendation engine that serves predictions with <100ms latency. What's the best approach for model selection, trai

AI ethics and responsible AI development specialist. Use when reviewing an AI system for bias, fairness violations, or regulatory compliance gaps; when generating a model card, algorithmic impact assessment, or ethics review document; or when an AI feature touches a protected class or high-stakes domain (hiring, healthcare, credit, law enforcement).

Unified Comet Opik agent for instrumenting LLM apps, managing prompts/projects, auditing prompts, and investigating traces/metrics via the latest Opik MCP server.

Computer vision and image processing specialist. Use PROACTIVELY for image analysis, object detection, face recognition, OCR implementation, and visual AI applications.

Use this agent when you need to analyze data patterns, build predictive models, or extract statistical insights from datasets. Invoke this agent for exploratory analysis, hypothesis testing, machine learning model development, and translating findings into business recommendations. Specifically:\\n\\n<example>\\nContext: Product team wants to understand why customer churn increased 15% last month and identify actionable retention levers.\\nuser: \"We're seeing higher churn recently. Can you anal

Specialized agent

Beast Mode 2.0: A powerful autonomous agent tuned specifically for GPT-5 that can solve complex problems by using tools, conducting research, and iterating until the problem is fully resolved.

Use when designing LLM systems for production, implementing fine-tuning or RAG architectures, optimizing inference serving infrastructure, or managing multi-model deployments. Specifically:\\n\\n<example>\\nContext: A startup needs to deploy a custom LLM application with sub-200ms latency, fine-tuned on domain-specific data\\nuser: \"Design a production LLM architecture that supports our use case with sub-200ms P95 latency, includes fine-tuning capability, and optimizes for cost\"\\nassistant: \

Use this agent when you need to red-team a Large Language Model deployment — jailbreak probes, prompt injection harness design, output-safety evaluation, and robustness evidence for EU AI Act Article 15 or NIST AI RMF MEASURE-2.7. Covers cloud-hosted models and on-prem / air-gapped local models (Ollama, vLLM, llama.cpp). Specifically:\\n\\n<example>\\nContext: A healthcare vendor embeds an LLM in a clinical triage tool and the compliance team wants a red-team report before rollout.\\nuser: \"We'

LLMs.txt roadmap file generator and maintainer for AI Engine Optimization (AEO). Use after build completion, content changes, or when setting up AI crawler navigation for a site. Detects framework, scans site structure, and writes a spec-compliant llms.txt file.

Use this agent when you need to deploy, optimize, or serve machine learning models at scale in production environments. Specifically:\\n\\n<example>\\nContext: User has a trained ML model and needs to deploy it to handle real-time inference requests with minimal latency.\\nuser: \"I have a PyTorch model that needs to serve 1000+ requests per second. What's the best way to deploy this?\"\\nassistant: \"I'll use the machine-learning-engineer agent to analyze your model, optimize it for inference,

Use this agent when you need to build, debug, or optimize Model Context Protocol (MCP) servers and clients that connect AI systems to external tools and data sources. Specifically:\\n\\n<example>\\nContext: User is building an MCP server to expose database tools to Claude.\\nuser: \"I need to create an MCP server that lets Claude query our PostgreSQL database and run analytical reports.\"\\nassistant: \"I'll use the mcp-developer agent to build a production-ready MCP server with proper schema va

Model Context Protocol (MCP) integration specialist for the cli-tool components system. Use PROACTIVELY for MCP server configurations, protocol specifications, and integration patterns.

Expert assistant for building MCP-based declarative agents for Microsoft 365 Copilot with Model Context Protocol integration

Activate your personal Microsoft/Azure tutor - learn through guided discovery, not just answers.

Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining. Specifically:\\n\\n<example>\\nContext: A team needs to implement a complete ML system that trains a recommendation model, serves predictions at scale, and monitors for performance degradation.\\nuser: \"We need to build an ML pipeline that trains a collaborative filtering model on 100M user events daily, serves predictions sub-1

AI model evaluation and benchmarking specialist. Use when selecting the right model for a specific task, designing evaluation benchmarks from scratch, or running post-deployment regression testing. Specifically:\n\n<example>\nContext: A product team needs to choose between Claude Sonnet, GPT-4o, and Gemini 1.5 Pro for a customer support summarization pipeline with a $500/month budget\nuser: \"We need to pick a model for our customer support summarization system. We process 50k tickets/month and

Use when building production NLP systems, implementing text processing pipelines, developing language models, or solving domain-specific NLP tasks like named entity recognition, sentiment analysis, or machine translation. Specifically:\\n\\n<example>\\nContext: E-commerce platform needs to automatically categorize customer reviews into product categories and extract sentiment\\nuser: \"We have 500K customer reviews. Build an NLP pipeline to categorize them by product and extract sentiment with c

Expert in Power Platform custom connector development with MCP integration for Copilot Studio - comprehensive knowledge of schemas, protocols, and integration patterns

Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai

Use this agent when you need to design, optimize, test, or evaluate prompts for Claude (or other LLMs) in production systems. Specifically:\n\n<example>\nContext: You're building a customer support chatbot and need to create high-quality prompts that balance accuracy, cost, and response speed.\nuser: \"I need to optimize prompts for our support bot. We're getting 82% accuracy now, but we want to reach 95% and reduce token usage by at least 30%.\"\nassistant: \"I'll help you design and optimize y

A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It evaluates the prompt against a systematic framework of prompt engineering best practices, then generates a new improved prompt. Use this agent when you need to turn vague or incomplete instructions into precise, production-ready system prompts. <example> <context>User routes a vague instruction to this agent via an orchestrator.</context> user: summarize emails assistant: <reas

Responsible AI specialist ensuring AI works for everyone through bias prevention, accessibility compliance, ethical development, and inclusive design