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 vasilyu1983/AI-Agents-public --skill ai-distributed-traininggit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-distributed-training)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-distributed-training"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-distributed-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/vasilyu1983/ai-agents-public/ai-distributed-training"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-distributed-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 | $0.00057 | $0.10326 |
| Opus 5 | $0.00028 | $0.05163 |
| Sonnet 5 | $0.00011 | $0.02065 |
| Haiku 4.5 | $0.00006 | $0.01033 |
Grade A, and why
ai-distributed-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 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 — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Distributed Training - Systems Performance Skill
Operational focus: picking and implementing the right parallelism strategy, not the theory. Covers data parallelism through FSDP/ZeRO/tensor+pipeline parallelism, memory-efficient attention, mixed precision at scale, activation checkpointing, rented-GPU cost discipline, and reproducing GPT-2 124M as the canonical sanity check.
Profile before you scale. Debug on the smallest GPU that fits. Stop the instance when done.
ASCII Flow
single GPU (debug/prototype)
└─ DDP: replicate model, all-reduce gradients — scales until the gradient all-reduce stops hiding behind compute
└─ FSDP2 / ZeRO: shard optimizer state, gradients, params across GPUs
└─ tensor parallelism: split weight matrices across GPUs (intra-node)
└─ pipeline parallelism: split layers across nodes (inter-node)
└─ context parallelism: shard the sequence dim (long context)
└─ expert parallelism: route MoE experts across GPUs (all-to-all)
└─ N-D parallelism: DP + TP + PP + CP + EP (frontier MoE)
profile-before-scale
└─ nsys / torch.profiler → find bottleneck (compute? memory? dataloader?)
└─ fix bottleneck at small scale, then scale
When to Use This Skill
Activate when the user asks about:
- Choosing between DDP, FSDP2, DeepSpeed ZeRO stages 1/2/3, or Megatron-LM
- Training Mixture-of-Experts (MoE) models: expert parallelism, all-to-all, load balancing
- OOM errors on multi-GPU training runs
- Memory-efficient attention (FlashAttention-2/3, xformers)
- Mixed precision (bf16, fp8, nvfp4) trade-offs at pre-training scale
- Optimizer choice at scale (AdamW vs Muon/MuonClip)
- Targeting current-gen hardware (H100, Blackwell B200/GB200 NVL72, early Rubin NVL72 access)
- Gradient checkpointing vs activation checkpointing cost
- Pre-training frameworks: litgpt, torchtitan, nanotron, levanter
- Reproducing GPT-2 (modded-nanoGPT or nanochat as the active reference; llm.c as the educational one)
- Rented GPU cost management (RunPod, Lambda, Vast.ai, Modal)
- Spot / interruptible instance checkpoint strategies
- Profiling a training run before deciding to scale
What ships with it
8 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.
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 Changed · +94 lines ce6e04199c9a
- 11d ago First seen · 336 lines · 57 tokens per session scan A 659e66d7ffc0
ai-distributed-training is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 9d ago), licensed MIT. It adds 57 tokens to every session and 10,326 once invoked, about $0.0003 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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