Official agent skill

Nemo Mbridge Perf Moe Comm Overlap

MoE expert-parallel communication overlap in Megatron Bridge.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Nemo Mbridge Perf Moe Comm Overlap

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-comm-overlap --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-comm-overlap .claude/skills/nemo-mbridge-perf-moe-comm-overlap && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "nemo-mbridge-perf-moe-comm-overlap" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap into .claude/skills/nemo-mbridge-perf-moe-comm-overlap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-comm-overlap", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-comm-overlap --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-comm-overlap .agents/skills/nemo-mbridge-perf-moe-comm-overlap && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "nemo-mbridge-perf-moe-comm-overlap" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap into .agents/skills/nemo-mbridge-perf-moe-comm-overlap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-comm-overlap", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-comm-overlap --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-comm-overlap .cursor/skills/nemo-mbridge-perf-moe-comm-overlap && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "nemo-mbridge-perf-moe-comm-overlap" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap into .cursor/skills/nemo-mbridge-perf-moe-comm-overlap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-comm-overlap", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/nemo-mbridge-perf-moe-comm-overlap

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-comm-overlap --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-comm-overlap .gemini/skills/nemo-mbridge-perf-moe-comm-overlap && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "nemo-mbridge-perf-moe-comm-overlap" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap into .gemini/skills/nemo-mbridge-perf-moe-comm-overlap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-comm-overlap", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-comm-overlap

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-comm-overlap .github/skills/nemo-mbridge-perf-moe-comm-overlap && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "nemo-mbridge-perf-moe-comm-overlap" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap into .github/skills/nemo-mbridge-perf-moe-comm-overlap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-comm-overlap", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-comm-overlap --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-comm-overlap .opencode/skills/nemo-mbridge-perf-moe-comm-overlap && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "nemo-mbridge-perf-moe-comm-overlap" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-comm-overlap into .opencode/skills/nemo-mbridge-perf-moe-comm-overlap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-comm-overlap", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: nemo-mbridge-perf-moe-comm-overlap
GitHub stars: 3.6k
Token cost: ~1.9k tokens
SKILL.md length: 771 words
Files: 6
Skills in repo: 390
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

MoE expert-parallel communication overlap in Megatron Bridge.

Works in 6 steps: Shared expert overlap conflict:… → PP without VPP: MoE overlap requires VPP… → Flex != backend flag:… → …
AI & LLM Engineering work in your project
SKILL.md covers Quick Decision, Enablement, Recompute And CUDA Graph… and Measured Evidence, plus 3 more sections
Calls uv

What it does

Nemo Mbridge Perf Moe Comm Overlap is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. MoE expert-parallel communication overlap in Megatron Bridge. Covers dispatch/combine overlap, flex dispatcher backends, and expert wgrad scheduling.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `BENCHMARK.md`, `card.yaml` and `evals/evals.json`).

It sits in AI & LLM Engineering. It works with NVIDIA AI Platform and CUDA. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

AI & LLM Engineering work in your project

Example prompts

“/nemo-mbridge-perf-moe-comm-overlap”

Requirements

Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Shared expert overlap conflict: moe_shared_expert_overlap and
PP without VPP: MoE overlap requires VPP when pipeline parallelism is
Flex != backend flag: moe_flex_dispatcher_backend="deepep" alone
Conservative recipe defaults: Most public recipes leave MoE overlap
Performance gains are workload-dependent: overlap helps most when dispatch
Summed kernel time is not wall time: concurrent kernels can run longer

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nemo Mbridge Perf Moe Comm Overlap loads about 1.9k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 771 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~46
When it runs · the whole SKILL.md, loaded when a task matches: ~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 771 words, ~1,873 tokens.

Download SKILL.mdSave it as .claude/skills/nemo-mbridge-perf-moe-comm-overlap/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: nemo-mbridge-perf-moe-comm-overlap
description: MoE expert-parallel communication overlap in Megatron Bridge. Covers dispatch/combine overlap, flex dispatcher backends, and expert wgrad scheduling.
license: Apache-2.0
when_to_use: Tuning MoE communication overlap, or tracing a MoE throughput regression to a comm-overlap config change; 'overlap_moe_expert_parallel_comm', 'MoE dispatch…

MoE Communication Overlap

For the higher-level overview, see:

@docs/training/communication-overlap.md
@skills/nemo-mbridge-perf-moe-comm-overlap/card.yaml

Quick Decision

Use MoE communication overlap when:

EP > 1
token dispatch or combine time is visible in the profile
the run is already correct and you are now tuning throughput

Avoid turning it on as an early bring-up step. It is easier to validate after the dispatcher, routing mode, and recompute plan are already stable.

Enablement

python

cfg.comm_overlap.overlap_moe_expert_parallel_comm = True

# Optional: delayed wgrad for additional overlap
cfg.comm_overlap.delay_wgrad_compute = True

# IMPORTANT: disable shared expert overlap when using dispatch overlap
cfg.model.moe_shared_expert_overlap = False

Prerequisites

expert_model_parallel_size > 1
num_moe_experts > 1
moe_token_dispatcher_type must be "alltoall" or "flex"
Precision: BF16 or FP16
If PP is used, VPP (virtual_pipeline_model_parallel_size) must be set (non-None)

Flex dispatcher activation

Setting moe_flex_dispatcher_backend alone does not activate flex dispatch. You must also set moe_token_dispatcher_type = "flex".

Recompute And CUDA Graph Interaction

Full recompute is not a good companion for the overlap path.
delay_wgrad_compute adds further constraints if CUDA-graph scopes include attention or MoE-router work.
In practice, selective recompute is the safer pairing when overlap is enabled.

Measured Evidence

HybridEP production-shape validation

A 2026-07-25 controlled Qwen3 30B-A3B pretraining comparison used 16 H100 GPUs, BF16, sequence length 4096, TP=1, PP=1, CP=1, EP=16, MBS=1, GBS=1024, forced-balanced routing, HybridEP, and Transformer Engine CUDA-graph scopes moe_router and moe_preprocess. The only performance change was plain EP overlap; delayed wgrad stayed disabled.

Case	Steady window	Step time	Model TFLOPS/GPU
EP overlap off	iterations 5-20	24.7138s	244.039
EP overlap on, search run	iterations 5-20	21.0725s	286.208
EP overlap on, independent validation	iterations 41-50	20.9920s	287.305

The independent result reduced step time by 15.059% and increased throughput by 17.729% over the reproduced baseline. Loss remained finite, no iterations were skipped or NaN, and rank-0 peak allocated memory was 62.166 GiB.

A same-method rank-0 Nsight Systems comparison captured 463,348 kernels in each case:

Profile metric	Overlap off	Overlap on
Communication concurrent with GEMM/attention	9.079ms	3,958.997ms
Communication time hidden by compute	0.11%	36.55%
GPU-active interval union	22.821s	21.221s
HybridEP dispatch-with-permute NVTX	4.253s	1.767s
HybridEP metadata-preprocess NVTX	3.109s	0.670s

This is direct evidence that the gain came from hiding exposed HybridEP dispatch/combine work, not from changing the dispatcher, routing, graph scopes, batch shape, or parallel layout.

Correctness-first alltoall smoke

A 2026-05-18 current-main H100 x16 smoke on Qwen3 30B-A3B mock pretraining used EP=16, alltoall, global batch size 1024, CUDA graphs disabled, and moe_permute_fusion=false because the PyTorch 25.11 / TE / Triton stack failed in Transformer Engine fused permutation in prior bring-up.

Results were directional rather than release-grade:

no EP overlap: 41.25s steady-state mean over iterations 3-8
EP overlap: 31.31s steady-state mean over iterations 3-8
EP overlap plus delay_wgrad_compute: 31.20s steady-state mean over iterations 3-8

Treat this as evidence that EP overlap can help an inter-node alltoall MoE shape when communication is exposed. It is not proof that delayed wgrad is a separate win, and it does not validate the fused permutation path. An earlier 2026-05-16 short smoke on the same shape showed the same pattern.

Show full SKILL.md (309 more words)Show less

Code Anchors

Overlap validation: src/megatron/bridge/training/comm_overlap.py
Flex dispatcher backend: src/megatron/bridge/training/flex_dispatcher_backend.py
Config: src/megatron/bridge/training/config.py
Unit tests: tests/unit_tests/training/test_comm_overlap.py
DeepEP tests: tests/unit_tests/training/test_deepep.py

Pitfalls

Shared expert overlap conflict: moe_shared_expert_overlap and overlap_moe_expert_parallel_comm can conflict. Disable shared expert overlap when using the dispatch overlap path.
PP without VPP: MoE overlap requires VPP when pipeline parallelism is active. Without it, the overlap scheduling cannot interleave correctly.
Flex != backend flag: moe_flex_dispatcher_backend="deepep" alone does nothing if moe_token_dispatcher_type is still "alltoall".
Conservative recipe defaults: Most public recipes leave MoE overlap disabled. You need to explicitly enable it via overrides.
Performance gains are workload-dependent: overlap helps most when dispatch communication is already a visible slice of step time. It is not guaranteed to help every small or lightly loaded EP run.
Summed kernel time is not wall time: concurrent kernels can run longer because they contend for SMs or bandwidth, so overlap may increase summed per-stream kernel duration while reducing the exposed interval union and end-to-end step time.

Verification

Look for overlap-related log messages during initialization. The comm overlap validation in comm_overlap.py will raise if prerequisites are not met, so a clean startup confirms the feature is active.

For a short performance-harness smoke, keep the command shape explicit and vary only one overlap knob at a time:

bash

uv run python scripts/performance/run_script.py \
  -m qwen \
  -mr qwen3_30b_a3b \
  --task pretrain \
  -g h100 \
  -c bf16 \
  -ng 16 \
  -gn 8 \
  --max_steps 8 \
  --cuda_graph_impl none \
  --moe_flex_dispatcher_backend None \
  --moe_a2a_overlap false \
  --tokenizer_type NullTokenizer \
  comm_overlap.overlap_moe_expert_parallel_comm=true \
  comm_overlap.delay_wgrad_compute=false \
  model.moe_shared_expert_overlap=false

If fused MoE permutation fails during bring-up, add model.moe_permute_fusion=false to separate overlap timing from runtime-stack validation, then retest with the matched production container.

For performance validation, use an unprofiled steady window as the acceptance metric. Use a matched Nsight A/B to establish causality:

Keep dispatcher, routing, CUDA graphs, batch shape, parallelism, and runtime fixed.
Toggle only overlap_moe_expert_parallel_comm; keep delay_wgrad_compute=false for the first isolation.
Compare communication and compute interval unions and their intersection, not only summed kernel durations.
Report steady step time, model TFLOPS/GPU, loss finiteness, skipped/NaN iterations, and peak allocated memory.

Last signature refresh: 2026-08-03.

Files

SKILL.md and 5 other files in skills/nemo-mbridge-perf-moe-comm-overlap of NVIDIA/skills.

SKILL.md
BENCHMARK.md
card.yaml
evals/evals.json
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 14a98ae

Compare with similar skills

Nemo Mbridge Perf Moe Comm Overlap next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Questions about Nemo Mbridge Perf Moe Comm Overlap

What does Nemo Mbridge Perf Moe Comm Overlap do?

MoE expert-parallel communication overlap in Megatron Bridge. Nemo Mbridge Perf Moe Comm Overlap is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. MoE expert-parallel communication overlap in Megatron Bridge.

When should I use Nemo Mbridge Perf Moe Comm Overlap?

Nemo Mbridge Perf Moe Comm Overlap fits situations like: AI & LLM Engineering work in your project.

How do I install Nemo Mbridge Perf Moe Comm Overlap in Claude Code?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a claude-code`. Or copy the skill folder (skills/nemo-mbridge-perf-moe-comm-overlap in NVIDIA/skills) into .claude/skills/nemo-mbridge-perf-moe-comm-overlap in your project. Claude Code loads it when a task matches its description.

How do I install Nemo Mbridge Perf Moe Comm Overlap in Codex?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a codex`. Or copy the skill folder (skills/nemo-mbridge-perf-moe-comm-overlap in NVIDIA/skills) into .agents/skills/nemo-mbridge-perf-moe-comm-overlap in your project. Codex loads it when a task matches its description.

Can I use Nemo Mbridge Perf Moe Comm Overlap in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemo-mbridge-perf-moe-comm-overlap, .gemini/skills/nemo-mbridge-perf-moe-comm-overlap, .github/skills/nemo-mbridge-perf-moe-comm-overlap and .opencode/skills/nemo-mbridge-perf-moe-comm-overlap in your project.

What does Nemo Mbridge Perf Moe Comm Overlap need to run?

Going by SKILL.md and its folder, Nemo Mbridge Perf Moe Comm Overlap needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Nemo Mbridge Perf Moe Comm Overlap access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Nemo Mbridge Perf Moe Comm Overlap safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nemo Mbridge Perf Moe Comm Overlap use?

Nemo Mbridge Perf Moe Comm Overlap is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nemo Mbridge Perf Moe Comm Overlap use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nemo Mbridge Perf Moe Comm Overlap?

Skills that share tags, products or a category with Nemo Mbridge Perf Moe Comm Overlap: Graphsignal (graphsignal/graphsignal, 257 stars), LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 938 stars), Optimize Op (CVCUDA/CV-CUDA, 2.7k stars) and Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemo Mbridge Perf Moe Comm Overlap?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Nemo Mbridge Perf Moe Comm Overlap

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

MoE Communication Overlap

Quick Decision

Enablement

Prerequisites

Flex dispatcher activation

Recompute And CUDA Graph Interaction

Measured Evidence

HybridEP production-shape validation

Correctness-first alltoall smoke

Code Anchors

Pitfalls

Verification

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