TorchedHat

Refactor a PyTorch test file to be device-agnostic. Walks through analyzing test classes, classifying them (GENERIC/DEVICE_GENERIC/DEVICE_SPECIFIC/MULTI_DEVICE_GENERIC/MULTI_DEVICE_SPECIFIC), splitting mixed classes, adding hw_classification attributes, converting hardcoded device references, and verifying the refactoring. Use when refactoring any test file in the PyTorch repo for the device-agnostic testing initiative.

Reference for PyTorch distributed training hang patterns, NCCL communicator architecture, flight recorder output format, debug logging levels, and timeout configuration. Covers barrier deadlocks, p2p hangs after communicator abort, init timeouts from store, collective mismatches, and DDP unused parameter hangs. Includes ARCHITECTURE.md with source-level ProcessGroupNCCL internals (communicator lifecycle, watchdog, work queue, store-based init). Use as knowledge base for diagnosing why distributed training freezes.

Test skill that runs a script from inside Claude. Use to verify that Claude can execute plugin-bundled scripts via ${CLAUDE_PLUGIN_ROOT}.

Expert guidance for vLLM's custom compiler - focusing on @support_torch_compile decorator, vllmBackend/Inductor Passes, PiecewiseBackend, and CudaGraphWrapper. Use when debugging torch.compile issues within vllm, implementing fusion passes, configuring graph splitting, investigating guard dropping, or working with CUDA graph capture in vLLM.

Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.

Create new Claude Code plugins from scratch with proper directory structure (.claude-plugin/plugin.json, skills/, agents/), or add existing plugins to marketplace.json. Use when creating a plugin, setting up plugin metadata, configuring MCP servers, or adding plugins to a marketplace.

Create and update agent definition files following Anthropic best practices. Use when writing agent files, creating subagents, designing agent systems, or troubleshooting agent discovery and delegation issues.

Guide users through creating Agent Skills for Claude Code. Use when the user wants to create, write, author, or design a new Skill, or needs help with SKILL.md files, frontmatter, or skill structure.

Analyze PyTorch internals across Python, C++, and CUDA layers. Use when asked about how PyTorch operators work internally, where functions are implemented (CPU/CUDA backends), what would break if code is modified (impact analysis), how torch.nn modules connect to native code, or finding tests for PyTorch operators. Covers ATen ops, nn.Module classes, dispatch mechanisms, and test infrastructure.