From TorchTalk
Traces PyTorch operator implementations across Python, C++, and CUDA layers, analyzes nn.Module-to-native connections, maps code changes to affected tests, and queries dispatch mechanisms.
How this skill is triggered — by the user, by Claude, or both
Slash command
/torchtalk:torchtalk-analyzerThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
- "How does torch.X work internally?"
get_status() # Check what's loaded and available
| Tool | Use For |
|---|---|
trace(name, focus?) | Trace any PyTorch op: Python → YAML → C++ → file:line |
search(query, mode?, backend?) | mode="bindings": find dispatch registrations. mode="kernels": find CUDA kernel launches |
graph(name, mode?, depth?, fuzzy_all_levels?, walk_python?, focus?) | mode="callers": inbound. mode="calls": outbound. mode="impact": transitive callers (depth/walk_python/focus apply to impact only) |
modules(name, mode?, focus?) | mode="trace": class details (focus="full" adds bases/docstring). mode="list": browse by category ("nn", "optim", "all") |
tests(query?, mode?, limit?, focus?) | mode="find": search tests (focus narrows to functions/classes/files). mode="utils": list utilities (query/focus ignored). mode="file_info": test file details |
affected(funcs, depth?) | Map changed C++ functions (comma-separated) to impacted Python test files |
trace("matmul") # Get binding chain
graph("matmul", mode="calls") # See internal dependencies
graph("gemm", mode="impact", depth=4)
search("conv2d", backend="CUDA")
trace("conv2d", focus="dispatch")
modules("Linear") # Python class details
trace("linear") # Underlying ATen op
tests("softmax")
compile_commands.json)Run get_status() to check availability.
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