Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-Bridgenpx agentmods add skills/nvidia-nemo/megatron-bridge/nemo-mbridge-recipe-recommenderWrote 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/nvidia-nemo/megatron-bridge/nemo-mbridge-recipe-recommender)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-recipe-recommender"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-recipe-recommender/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/nvidia-nemo/megatron-bridge/nemo-mbridge-recipe-recommender"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-recipe-recommender.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 | $0.00080 | $0.04611 |
| Opus 5 | $0.00040 | $0.02305 |
| Sonnet 5 | $0.00016 | $0.00922 |
| Haiku 4.5 | $0.00008 | $0.00461 |
Grade A, and why
nemo-mbridge-recipe-recommender 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 — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Recipe — Recipe Index & Recommendation
This skill indexes every shipped recipe and helps users pick the right starting config, adjust parallelism, and avoid common pitfalls.
How to Use This Skill
- Ask the user for: model name/size, GPU count & type, training goal (pretrain / SFT / PEFT), and sequence length (if non-default).
- Look up the best-match recipe in the index below.
- Recommend the recipe function name + entry-point command.
- Provide adjustment advice (parallelism resizing, batch tuning, pitfalls).
First Answer Checklist
When recommending recipes, always include these distinctions before the long index details:
- Library recipes under
src/megatron/bridge/recipes/are for functional training and usescripts/training/run_recipe.py. - Benchmark recipes under
src/megatron/bridge/perf_recipes/are for upper-bound throughput benchmarks. They own their canonical benchmark data and settings and should not be presented as production training recipes. - For a first-time Bridge smoke test, recommend
llama3_8b_pretrain_configwith mock data via--dataset mock. - For normal SFT recommendations, select a finetuning preset such as
--dataset squador--dataset tulu3; for pretrain and mock validation recommendations, use--dataset mock. Do not pair the pretraining-onlymockpreset with an SFT or PEFT mode. - After the recipe and dataset, give the required resizing rules: TP must
divide
num_key_value_heads, use the lowest TP that still fits the dense state, keep TP within one node unless using NVL72-class interconnect, enable SP when TP > 1, configure CP for long context, and make both dense and expert meshes divide the allocation. - State whether each proposed override changes the convergence contract or only the execution/performance mapping. Do not trade convergence semantics for throughput without calling it a new experiment.
Configuration Layers and Change Control
What ships with it
5 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.
- 12d ago First seen · 371 lines · 80 tokens per session scan A 185fa783a065
nemo-mbridge-recipe-recommender is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (905 stars, last pushed yesterday), licensed Apache-2.0. It adds 80 tokens to every session and 4,611 once invoked, about $0.0004 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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