AI & LLM Integration for Claude Code

Google Model Armor: Sanitize a user prompt through a Model Armor template.

Google Model Armor: Sanitize a model response through a Model Armor template.

Best practices for HeyGen - AI avatar video creation API

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Expert patterns for HubSpot CRM integration including OAuth authentication, CRM objects, associations, batch operations, webhooks, and custom objects. Covers Node.js and Python SDKs. Use when: hubspot, hubspot api, hubspot crm, hubspot integration, contacts api.

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

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: \

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

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.

Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.

Use when the user asks to generate images via the Luma AI API (Dream Machine / Photon); collects a prompt and options interactively, then calls the API using the bundled script. Requires LUMA_API_KEY — will prompt the user if missing.

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,

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.

This skill should be used when the user asks to "add MCP server", "integrate MCP", "configure MCP in plugin", "use .mcp.json", "set up Model Context Protocol", "connect external service", mentions "${CLAUDE_PLUGIN_ROOT} with MCP", or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into Claude Code plugins for external tool and service integration.

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

Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we discuss", "remember when", "find previous conversation", "check history", or before starting work to recall prior decisions.

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

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

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

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

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

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

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.