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 open-infra-skills/infra-skills --skill optimize-musa-traininggit clone --depth 1 https://github.com/open-infra-skills/infra-skillsWrote 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/open-infra-skills/infra-skills/optimize-musa-training)<a href="https://agentmods.dev/skills/open-infra-skills/infra-skills/optimize-musa-training"><img src="https://agentmods.dev/badge/skills/open-infra-skills/infra-skills/optimize-musa-training/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/open-infra-skills/infra-skills/optimize-musa-training"><img src="https://agentmods.dev/badge/skills/open-infra-skills/infra-skills/optimize-musa-training.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 | $0.00110 | $0.01481 |
| Opus 5 | $0.00055 | $0.00740 |
| Sonnet 5 | $0.00022 | $0.00296 |
| Haiku 4.5 | $0.00011 | $0.00148 |
Grade A, and why
optimize-musa-training 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 12d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize MUSA Training
Use a measurement-first workflow to improve MUSA training throughput without changing model semantics. Treat framework timing, system traces, and kernel counters as different layers of evidence.
Guardrails
- Preserve model architecture, data semantics, optimizer math, precision policy, and checkpoint compatibility unless the user explicitly authorizes a change.
- Establish a versioned baseline before editing code. Compare forward outputs, loss, gradients, memory, and steady-state throughput after every retained change.
- Keep profiler overhead out of the throughput denominator. Measure FLOPs in a profiled run and steady step time in an otherwise equivalent non-profiled run.
- Never label profiler-attributed executed FLOPs as model MFU without stating the FLOP definition and coverage. Distinguish useful model FLOPs, executed hardware FLOPs, and profiler-attributed FLOPs.
- Do not infer MUSA behavior from CUDA behavior. Feature-detect the installed driver, SDK, Torch MUSA, muDNN, muBLAS, MCCL, attention backend, and profiler versions.
- Keep cluster transport separate from profiling logic. Do not require PowerShell, VS Code, a jump host, a specific scheduler, or a particular client operating system.
- Keep credentials, internal hostnames, private image registries, dataset paths, and proprietary reports out of public artifacts.
Route The Task
- For environment, import, device-selection, or container failures, read environment-and-preflight.md.
- For MFU/HFU, PyTorch profiling, timeline analysis, transfers, or kernel counters, read measurement-and-profiling.md.
- For FA2, GEMM, compile, launch, memory, dataloader, FSDP, or MCCL optimization, read optimization-playbook.md.
- Before retaining any optimization, read correctness-and-experiments.md.
- For a real low-batch S5000 case and negative results worth avoiding, read s5000-case-study.md.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 265 B
- references/correctness-and-experiments.md 3.8 KB
- references/environment-and-preflight.md 4.4 KB
- references/measurement-and-profiling.md 5.2 KB
- references/optimization-playbook.md 6.2 KB
- references/s5000-case-study.md 4.7 KB
- scripts/compute_mfu.py 5.9 KB runs code
- scripts/musa_env_report.py 5.1 KB runs code
- scripts/summarize_steps.py 4.8 KB runs code
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.
- 12d ago First seen · 135 lines · 110 tokens per session scan A b2b5a881f61c
optimize-musa-training is a skill published in the GitHub repository open-infra-skills/infra-skills (140 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 110 tokens to every session and 1,481 once invoked, about $0.0006 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 2026-08-30.
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