Skill Claude CodeCodex
Configure and troubleshoot NOOA model registry entries with Connect or the library, including endpoints, credentials, reply budgets, reasoning levels, cache reuse and reasoning replay. Use for model onboarding, editing aliases, or Connect diagnostic handoffs, not Agent class authoring.
Skill Claude CodeCodex
Prepare, validate, build, and use Nemotron Customizer airgap image bundles for offline clusters. Use when planning airgapped deployments, editing deploy/nemotron-customizer/airgap/airgap.yaml, selecting workflow targets, grouping step execution images, baking repo overlays or wheel additions, resuming airgap runner…
Skill Claude CodeCodex
Onboard a new model family (Nemotron or third-party) into skills/ — paper chunks, recipe summaries, context packs, and model card. Use when a contributor wants downstream skills like /nemotron-customize to be able to route to a new model.
Skill Claude CodeCodex needs its repo
Add a cross-cutting decision pattern under src/nemotron/steps/patterns/. Use when a recurring ML decision (tokenizer lock, eval bookends, LoRA-on-small-data, etc.) must be encoded so other skills can fire it during planning.
Skill Claude CodeCodex
Add a new step under src/nemotron/steps/ / / — manifest (step.toml), runner glue, configs, and per-step README.md. Use when extending the catalog so /nemotron-customize can route to it.
Plugin Claude Code
Bundles 1 skill · 159 tokens together
Plan and configure repo-native Nemotron customization workflows from existing steps: curate/nemocurator JSONL cleaning, translate/nemocurator corpus translation, sft/automodel, peft/automodel, sft/megatronbridge, peft/megatronbridge, pretrain/CPT, rl/nemorl alignment, byob/mcq benchmarks, convert/megatrontohf an.
Skill Claude CodeCodex
Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment. Use when the user asks facts about the model rather than building a pipeline.
Skill Claude CodeCodex
Generates BYO custom safety policies for NVIDIA Nemotron content-safety guardrails — Nemotron-Content-Safety-Reasoning-4B (text) and multimodal Nemotron-3-Content-Safety. Produces a Markdown policy, JSON taxonomy, and drop-in inference prompts. Maps rough words or an existing policy to V2 categories, adding custom…
Skill Claude CodeCodex
Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron embed/rerank retrieval recipes.
Skill Claude CodeCodex
Reference desk for NVIDIA Nemotron 3 Super — architecture, training data, recipes (pretrain/SFT/RL/eval/quantization), and deployment notes. Use when the user asks facts about Super3 rather than building a pipeline.
Skill Claude CodeCodex
Reference desk for NVIDIA Nemotron 3 Ultra (550B-A55B) — architecture, NVFP4 pretraining, SFT, MOPD (multi-teacher on-policy distillation), MTP boosting, quantization, inference. Use when the user asks facts about Ultra rather than building a pipeline.
Skill Claude CodeCodex
Run the Nemotron-3 Ultra Text2SQL LoRA fine-tuning tutorial (NeMo Megatron-Bridge) end-to-end for the user on their SLURM cluster: data prep, distributed checkpoint conversion, and packed LoRA fine-tuning of the 550B hybrid Mamba-Transformer MoE, ending at a saved adapter. Use when the user wants to run this cookbook…
Skill Claude CodeCodex
Review Nemotron pull requests for critical bugs, tests, and documentation affected by the change.
Skill Claude CodeCodex
Reference desk for NVIDIA Nemotron 3.5 Lightning (30B-A3B), a hybrid Mamba-Transformer MoE with MTP built from Nano 3 Base by distillation from Super 3 Base, single-stage text SFT, RLVR, MTP boosting, and W4A16 NVFP4 quantization. Use when the user asks facts about Lightning 3.5 rather than building a pipeline.
Skill Claude CodeCodex
Reference desk for NVIDIA Nemotron 3.5 Super (120B-A12B), covering vision-language architecture, MTP, distillation pretraining from Ultra 3 Base, Indic CPT, SFT, RLVR, specialist teachers, two-phase MOPD, MTP boosting, NVFP4 quantization, and inference. Use when the user asks facts about Super 3.5 rather than building…
Skill Claude CodeCodex needs its repo
Guide for adding support for new LLM or VLM models in Megatron-Bridge. Covers bridge, builder-backed model config, recipe, tests, docs, and examples.
Skill Claude Code needs its repo
Dev environment setup for Megatron Bridge — container-based development, uv package management, lockfile regeneration, adding dependencies, Slurm container usage, and common build pitfalls.
Skill Claude Code
Bump a pinned dependency (TransformerEngine, Megatron-LM, NRX, etc.), regenerate the lockfile, open a PR, and drive it to green by attaching a watchdog to the "CICD NeMo" workflow and quarantining failing functional tests as flaky until the run is green.
Skill Claude Code
CI/CD reference for Megatron Bridge — pipeline structure, commit and PR workflow, CI failure investigation, and common failure patterns.
Skill Claude CodeCodex needs its repo
Create or update concise, agent-readable Megatron Bridge model verification cards. Use when adding a model support card, auditing cross-model convergence comparability or verification coverage, recording conversion, deterministic inference, training, checkpoint resume, post-SFT export, performance, or weak-scaling…
Skill Claude Code
Code style and quality rules for Megatron Bridge — ruff configuration, naming conventions, type hints, mypy rules, docstrings, copyright headers, logging, and the code review checklist.
Skill Claude Code
Analyze and compare PyTorch CUDA memory snapshots produced by Megatron Bridge's ProfilingConfig(recordmemoryhistory=True). Replays the recorded allocation timeline to plot memory over time, compare two runs, and attribute peak memory to source code locations.
Skill Claude Code needs its repo
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.
Skill Claude Code
Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.
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