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.
git clone --depth 1 https://github.com/masrisystems/dot-agentsWrote 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/agents/masrisystems/dot-agents/pytorch-build-resolver)<a href="https://agentmods.dev/agents/masrisystems/dot-agents/pytorch-build-resolver"><img src="https://agentmods.dev/badge/agents/masrisystems/dot-agents/pytorch-build-resolver/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/agents/masrisystems/dot-agents/pytorch-build-resolver"><img src="https://agentmods.dev/badge/agents/masrisystems/dot-agents/pytorch-build-resolver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00052 | $0.01114 |
| Opus 5.5 | $0.00021 | $0.00446 |
| Sonnet 5.5 | $0.00010 | $0.00223 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
pytorch-build-resolver 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 1mo 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 1mo ago First seen · 102 lines · 52 tokens per session scan A e02843f9bf66
pytorch-build-resolver is an agent published in the GitHub repository masrisystems/dot-agents (0 stars, last pushed 1mo ago), with no licence file. It adds 52 tokens to every session and 1,114 once invoked, about $0.0002 per session on Opus 5.5. 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 agents, from other repositories
root-cause-debugger
Investigation specialist that finds and PROVES the root cause of a bug before any code is changed. Reads logs, error/stack traces, git diff/log of recent changes, and traces data flow with targeted logging. Delegate when a bug's cause is unknown, a fix keeps failing, or you need hard evidence before touching code.…
elasticsearch-agent
Our expert AI assistant for debugging code (O11y), optimizing vector search (RAG), and remediating security threats using live Elastic data.
pytorch-build-resolver
PyTorch runtime, CUDA, and training error resolution specialist. Fixes tensor shape mismatches, device errors, gradient issues, DataLoader problems, and mixed precision failures with minimal changes. Use when PyTorch training or inference crashes.
antigravity-pair
Proactively use when Claude Code should hand a substantial build/debug/refactor task to Google Antigravity (Gemini 3.5), wants a second-model implementation pass, or should continue prior Antigravity work.
pytorch-build-resolver
PyTorch runtime, CUDA, and training error resolution specialist. Fixes tensor shape mismatches, device errors, gradient issues, DataLoader problems, and mixed precision failures with minimal changes. Use when PyTorch training or inference crashes.
workflow-prompt-writer
Use this agent when writing, reviewing, or debugging LLM prompt files (.prompt). Specializes in Liquid.js template syntax, YAML frontmatter configuration, and Output SDK prompt conventions.