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/adding-model-supportWrote 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/adding-model-support)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/adding-model-support"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/adding-model-support/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/adding-model-support"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/adding-model-support.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 | $0.00039 | $0.06560 |
| Opus 5.5 | $0.00016 | $0.02624 |
| Sonnet 5.5 | $0.00008 | $0.01312 |
| Haiku 4.5 | $0.00004 | $0.00656 |
Grade A, and why
adding-model-support 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 2d 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 — 573 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adding New Model Support in Megatron-Bridge
Implementation boundaries
- Keep model-support changes in Megatron-Bridge. Do not modify Megatron-Core or its submodule to implement a model. Use existing module specs, builders, conversion hooks, and narrow Bridge subclasses or compositions.
- Before adding a block, inspect both MCore and existing Bridge implementations. Reuse an existing block when it represents the same computation. Differences in accumulation precision, rounding, fused kernels, or mathematically equivalent LayerNorm/RMSNorm operation ordering alone do not justify a replacement implementation.
- Add model-specific code under the family's
modeling_<model>/directory only for genuinely missing computation or composition. For example, normalizing each expert before weighted summation differs mathematically from normalizing the combined result. Compose existing normalization and linear operations wherever possible instead of copying their kernels. - Invoke Hugging Face vision/video and audio encoders directly; do not port their internals into Bridge. Bridge owns their construction, checkpoint mapping, modality insertion, placement, freezing, and gradient synchronization. Existing native encoder ports are references for previously supported models, not a requirement for new support. Follow an explicit request for a different encoder implementation when the user provides one.
- Verify support through the requested model API, not only a shared config.
For HybridModel, trace the actual layer specs and expand per-layer schedules
to Hybrid positions:
*Erepeated N times has 2N positions. Configure RoPE explicitly, since HybridModel defaults to no positional embedding. - Separate weight-independent checks from checkpoint validation. A checkpoint download need not block implementation or small config/block tests; record full conversion, logits, and training validation as pending until they run.
Phase 1: Discovery
What ships with it
4 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.
- 2d ago Changed · +1 lines 1dae40da0150
- 11d ago Changed · +18 lines · +3 tokens per session 37c8b5a93ae7
- 25d ago Changed · +61 lines 555accc4e4f3
- 1mo ago Changed · +30 lines 4ae066aa5184
- 1mo ago First seen · 463 lines · 36 tokens per session scan A ad38996e271d
adding-model-support is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (970 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 6,560 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 2026-08-30.
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