Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add opendatahub-io/ai-helpers --skill torchtalk-tracegit clone --depth 1 https://github.com/opendatahub-io/ai-helpersWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/opendatahub-io/ai-helpers/torchtalk-trace)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/torchtalk-trace"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/torchtalk-trace/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/torchtalk-trace"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/torchtalk-trace.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00963 |
| Opus 5 | $0.00012 | $0.00481 |
| Sonnet 5 | $0.00005 | $0.00193 |
| Haiku 4.5 | $0.00002 | $0.00096 |
Grade A, and why
torchtalk-trace scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
odh-ai-helpers:torchtalk-trace
Synopsis
/torchtalk:trace <function-name>
/torchtalk:trace <function-name> full
/torchtalk:trace <function-name> dispatch
/torchtalk:trace <function-name> yaml
Description
The torchtalk:trace command traces a PyTorch function's complete binding chain from Python through C++ to CUDA implementations. It uses the TorchTalk MCP server to look up operator definitions in native_functions.yaml, find pybind11 and TORCH_LIBRARY bindings, and map dispatch keys to backend implementations.
After gathering the raw binding data, the command analyzes the results to explain:
- How the Python API connects to the native implementation
- Which dispatch keys route to which backends (CPU, CUDA, MPS, etc.)
- Where each layer is implemented with exact file:line references
- The autograd integration and backward pass formula (if applicable)
- Architectural context about why the dispatch is structured this way
This command requires the TorchTalk MCP server to be running. Run mcp__torchtalk__get_status to verify availability.
Prerequisites
- TorchTalk MCP server must be running and registered with Claude Code
- Run
/torchtalk:setupif not yet installed
Implementation
- Verify MCP server: Call
mcp__torchtalk__get_statusto confirm the TorchTalk server is running and has indexed data available - Trace binding chain: Call
mcp__torchtalk__tracewith the function name and optional focus parameter to retrieve the full binding chain - Get internal dependencies: Call
mcp__torchtalk__callswith the function name to understand what the function invokes internally - Analyze dispatch architecture: Examine the dispatch keys, backend routing, and structured binding patterns returned by the trace
- Synthesize explanation: Combine the trace data, call graph, and dispatch information into a coherent explanation that covers:
- The Python entry point and how it connects to native code
- The
native_functions.yamldefinition and its dispatch configuration - Each backend implementation with file:line references
- The autograd formula (from
derivatives.yaml) if applicable - Architectural observations about the operator's design
- Provide file references: Format all implementation locations as
file:linereferences so the developer can navigate directly to the source
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 86 lines · 24 tokens per session scan A eff5eb0d7bcf
torchtalk-trace is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 3d ago), licensed Apache-2.0. It adds 24 tokens to every session and 963 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
ampl-python
Expert in amplpy and Python integration with AMPL. Use when writing or refactoring amplpy scripts, notebooks, FastAPI services, data pipelines (pandas/polars), solver configuration via Python, result extraction, or Colab/MO-Book Python workflows. Knows amplpy.ampl.com and dev.ampl.com amplpy best practices.
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
manimgl-best-practices
Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains from manimlib import , (3) User runs manimgl CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown…
cuopt-developer
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.