torchtalk-analyzer

name	torchtalk-analyzer
description	Analyze PyTorch internals across Python, C++, and CUDA layers using the TorchTalk MCP server. Use when asked about how PyTorch operators work internally, where functions are implemented, what would break if code is modified, or finding tests for PyTorch operators.
allowed-tools	Bash Read Grep Glob

TorchTalk Analyzer

This skill enables cross-language analysis of PyTorch internals by leveraging the TorchTalk MCP server. It traces binding chains from Python through C++ to CUDA, analyzes dispatch mechanisms, maps call graphs, and locates test infrastructure.

When to Use

• When asked how a PyTorch operator works internally (e.g., "How does torch.add dispatch to CUDA?")
• When investigating where a function is implemented across CPU/CUDA/MPS backends
• When assessing what would break if a C++ function is modified (impact analysis)
• When tracing how torch.nn modules connect to native ATen operators
• When finding existing tests for a PyTorch operator or function
• When exploring PyTorch's dispatch architecture or autograd integration

Prerequisites

• TorchTalk MCP server must be running and registered with Claude Code
• PyTorch source code must be available locally
• Run /torchtalk:setup if TorchTalk is not yet installed

Verify availability:

mcp__torchtalk__get_status

If the status tool returns data, all tools below are ready.

Instructions

Step 1 - Verify MCP Server

Before using any tools, confirm the TorchTalk server is running:

mcp__torchtalk__get_status

Check that:

• Bindings are loaded (should show thousands of bindings)
• Native functions are parsed
• C++ call graph status is "Ready" (required for impact/calls/called_by)
• Python modules are loaded (required for trace_module/list_modules)

If the server is not available, direct the user to run /torchtalk:setup.

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Step 2 - Identify the Analysis Type

Match the user's question to the appropriate tool:

Question Pattern	Tool	Example
"How does X work?" / "Trace X"	mcp__torchtalk__trace	trace("softmax", "full")
"Find functions matching X"	mcp__torchtalk__search	search("conv", "CUDA")
"Where are the CUDA kernels for X?"	mcp__torchtalk__cuda_kernels	cuda_kernels("softmax")
"What does X call?"	mcp__torchtalk__calls	calls("at::native::add")
"What calls X?"	mcp__torchtalk__called_by	called_by("at::native::add")
"What breaks if I change X?"	mcp__torchtalk__impact	impact("at::native::add", 3)
"How does nn.Linear work?"	mcp__torchtalk__trace_module	trace_module("Linear")
"List all nn modules"	mcp__torchtalk__list_modules	list_modules("nn")
"Find tests for X"	mcp__torchtalk__find_similar_tests	find_similar_tests("softmax")
"What test utilities exist?"	mcp__torchtalk__list_test_utils	list_test_utils("all")
"What tests are in file X?"	mcp__torchtalk__test_file_info	test_file_info("test_torch")

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Step 3 - Execute and Synthesize

For simple lookups, a single tool call suffices. For deeper questions, combine multiple tools:

"How does torch.softmax work end-to-end?"

1. mcp__torchtalk__trace("softmax", "full") - Get the binding chain
2. mcp__torchtalk__cuda_kernels("softmax") - Find GPU kernels
3. mcp__torchtalk__calls("at::native::softmax") - See internal dependencies

"What breaks if I modify at::native::add?"

1. mcp__torchtalk__impact("at::native::add", 3) - Transitive callers
2. mcp__torchtalk__find_similar_tests("add") - Affected tests

"How does nn.Linear connect to native code?"

1. mcp__torchtalk__trace_module("Linear") - Module definition
2. mcp__torchtalk__trace("linear", "full") - Native operator chain

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Step 4 - Present Results

Format results with:

• Clear layer separation (Python -> YAML -> C++ -> CUDA)
• file:line references for every implementation location
• Architectural context explaining why the dispatch is structured this way
• Suggestions for further exploration if the user wants to go deeper

MCP Tools Reference

ATen Operators

Tool	Parameters	Description
mcp__torchtalk__trace	function_name, focus?	Trace Python to C++ binding chain. Focus: "full", "yaml", "dispatch"
mcp__torchtalk__search	query, backend?, limit?	Find bindings by name with optional backend filter
mcp__torchtalk__cuda_kernels	function_name?	Find GPU kernel launches with file:line

C++ Call Graph

Tool	Parameters	Description
mcp__torchtalk__impact	function_name, depth?	Transitive callers + Python entry points (depth 1-5)
mcp__torchtalk__calls	function_name	Functions this function invokes (outbound)
mcp__torchtalk__called_by	function_name	Functions that invoke this (inbound)

Python Modules

Tool	Parameters	Description
mcp__torchtalk__trace_module	module_name	Trace torch.nn.Linear, torch.optim.Adam, etc.
mcp__torchtalk__list_modules	category?	List modules: "nn" (default), "optim", "all", or search query

Test Infrastructure

Tool	Parameters	Description
mcp__torchtalk__find_similar_tests	query, limit?	Find tests for an operator or concept
mcp__torchtalk__list_test_utils	category?	List test utilities: "all" (default), "fixtures", "assertions", "decorators"
mcp__torchtalk__test_file_info	file_path	Details about a specific test file

Error Handling

• MCP server not running: Direct user to /torchtalk:setup
• "C++ call graph not available": PyTorch needs to be built once (python setup.py develop) to generate compile_commands.json
• "Function not found": Use mcp__torchtalk__search with partial names, or check spelling
• "Test infrastructure not loaded": Verify PyTorch source path is correct with torchtalk status