Content & Media

Instructs AI agents on documentation standards for design docs, folder READMEs, source code interfaces, and test cases

Use when designing or architecting Elixir/Phoenix applications, creating comprehensive project documentation, planning OTP supervision trees, defining domain models with Ash Framework, structuring multi-app projects with path-based dependencies, or preparing handoff documentation for Director/Implementor AI collaboration

Guides REST and GraphQL API design, including endpoints, error handling, versioning, and documentation. Use when designing APIs, creating endpoints, or when asked about API patterns.

Provides guidance on software architecture patterns and design decisions. Use when designing systems, choosing patterns, structuring projects, or when asked about architectural approaches.

Create comprehensive implementation plans with detailed file-level changes and test strategies

Guides performance analysis and optimization for any application. Use when diagnosing slowness, optimizing code, improving load times, or when asked about performance.

Designs comprehensive testing strategies for any codebase. Use when adding tests, improving coverage, setting up testing infrastructure, or when asked about testing approaches.

Process file changes and update CLAUDE.md memory sections. Use when the memory-updater agent needs to analyze dirty files, update AUTO-MANAGED sections, verify content removal, or detect stale commands. Invoked after file edits to keep project memory in sync.

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.

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

React/Tailwind component construction patterns for building reusable UI components.

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.

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

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.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

Game development orchestrator. Routes to platform-specific skills based on project needs.

Design thinking and decision-making for web UI. Use when designing components, layouts, color schemes, typography, or creating aesthetic interfaces. Teaches principles, not fixed values.

Mobile UX patterns for touch gestures, haptic feedback, accessibility, and platform-native interactions. Essential for building truly mobile-friendly apps.

Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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.