Official agent skill

Nemo Mbridge Resiliency

Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

OfficialApache-2.0Auto-check passed

Install Nemo Mbridge Resiliency

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-resiliency -a claude-code

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

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-resiliency --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-resiliency .claude/skills/nemo-mbridge-resiliency && 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-resiliency" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-resiliency into .claude/skills/nemo-mbridge-resiliency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-resiliency", 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-resiliency

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-resiliency -a codex

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

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-resiliency --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-resiliency .agents/skills/nemo-mbridge-resiliency && 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-resiliency" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-resiliency into .agents/skills/nemo-mbridge-resiliency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-resiliency", 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-resiliency -a cursor

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

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-resiliency --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-resiliency .cursor/skills/nemo-mbridge-resiliency && 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-resiliency" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-resiliency into .cursor/skills/nemo-mbridge-resiliency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-resiliency", 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-resiliency

--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-resiliency -a gemini-cli

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

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-resiliency --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-resiliency .gemini/skills/nemo-mbridge-resiliency && 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-resiliency" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-resiliency into .gemini/skills/nemo-mbridge-resiliency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-resiliency", 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-resiliency

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-resiliency -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-resiliency .github/skills/nemo-mbridge-resiliency && 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-resiliency" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-resiliency into .github/skills/nemo-mbridge-resiliency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-resiliency", 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-resiliency -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-resiliency --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-resiliency .opencode/skills/nemo-mbridge-resiliency && 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-resiliency" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-resiliency into .opencode/skills/nemo-mbridge-resiliency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-resiliency", 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-resiliency
GitHub stars: 3.5k
Token cost: ~2.8k tokens
SKILL.md length: 648 words
Files: 6
Skills in repo: 386
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

Works in 7 steps: ft_launcher, not torchrun: Direct… → Async save requires torch_dist:… → IPR + NeMo-Run: In-process restart is… → …
SKILL.md covers Enablement, Code Anchors, Pitfalls and Verification
Calls uv and pytest

What it does

Nemo Mbridge Resiliency is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

Its SKILL.md is about 2.8k 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 works with NVIDIA AI Platform. 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.

Example prompts

“/nemo-mbridge-resiliency”

Requirements

Python 3

Workflow steps

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

ft_launcher, not torchrun: Direct FaultToleranceConfig requires
Async save requires torch_dist: async_save=True only works with
IPR + NeMo-Run: In-process restart is not compatible with NeMo-Run
NVRx vs legacy straggler: Two detectors exist. Use NVRx
stop_if_detected default: NVRx logs but does not stop training by
NCCL watchdog vs hard_timeout: For IPR, NCCL watchdog timeout must
Rerun state machine is alpha: Use check_for_nan_in_loss=True for

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. 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
- pytest
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 Resiliency loads about 2.8k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 648 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~42
When it runs · the whole SKILL.md, loaded when a task matches: ~2.8k

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 dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 648 words, ~2,826 tokens.

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

name: nemo-mbridge-resiliency
description: Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.
license: Apache-2.0
when_to_use: Enabling resiliency features, or investigating a commit that caused training hangs, straggler detection failures, or broken restarts; 'fault tolerance'…

Resiliency

Stable docs: @docs/training/resiliency.md, @docs/training/checkpointing.md Card: @skills/nemo-mbridge-resiliency/card.yaml

Enablement

Fault tolerance (Slurm only)

Option 1: NeMo Run plugin (recommended)

python

from megatron.bridge.recipes.run_plugins import FaultTolerancePlugin
import nemo_run as run

task = run.Script(...)
run_plugins = [
    FaultTolerancePlugin(
        enable_ft_package=True,
        calc_ft_timeouts=True,
        num_in_job_restarts=3,
        num_job_retries_on_failure=2,
        initial_rank_heartbeat_timeout=1800,
        rank_heartbeat_timeout=300,
    )
]
run.run(task, plugins=run_plugins, executor=executor)

Plugin parameter	Default	Description
`num_in_job_restarts`	3	Max restarts within same job
`num_job_retries_on_failure`	2	Max new job launches on failure
`initial_rank_heartbeat_timeout`	1800	First heartbeat timeout (seconds)
`rank_heartbeat_timeout`	300	Subsequent heartbeat timeout (seconds)

Option 2: Direct config + ft_launcher

python

from megatron.bridge.training.config import FaultToleranceConfig

cfg.ft = FaultToleranceConfig(
    enable_ft_package=True,
    calc_ft_timeouts=True,
    simulate_fault=False,
    simulated_fault_type="random",
)

Launch with ft_launcher (not torchrun):

bash

export GROUP_RANK=0  # required for non-Slurm
ft_launcher \
    --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} \
    --nnodes=${NUM_NODES} --nproc-per-node=${NUM_GPUS_PER_NODE} \
    --ft-rank_section_timeouts=setup:600,step:180,checkpointing:420 \
    --ft-rank_out_of_section_timeout=300 \
    your_training_script.py

Config parameter	Default	Description
`enable_ft_package`	False	Enable fault tolerance
`calc_ft_timeouts`	False	Auto-compute optimal timeouts
`simulate_fault`	False	Enable fault simulation for testing
`simulated_fault_type`	`"random"`	`"rank_hung"`, `"rank_killed"`, or `"random"`
`simulated_fault_rank`	None	Specific rank to fault (random if None)
`simulated_fault_base_delay`	0	Base delay before simulating fault

Section-based timeout monitoring covers setup, training steps, checkpointing, and out-of-section time independently. Timeouts are saved to ft_state.json for subsequent runs when calc_ft_timeouts=True.

NVRx straggler detection

python

from megatron.bridge.training.config import NVRxStragglerDetectionConfig

cfg.nvrx_straggler = NVRxStragglerDetectionConfig(
    enabled=True,
    report_time_interval=300.0,
    calc_relative_gpu_perf=True,
    calc_individual_gpu_perf=True,
    num_gpu_perf_scores_to_print=5,
    gpu_relative_perf_threshold=0.7,
    gpu_individual_perf_threshold=0.7,
    stop_if_detected=False,
    enable_logging=True,
)

Parameter	Default	Description
`enabled`	False	Enable straggler detection
`report_time_interval`	300.0	Seconds between straggler checks
`calc_relative_gpu_perf`	True	Compare ranks against each other
`calc_individual_gpu_perf`	True	Track per-rank degradation over time
`gpu_relative_perf_threshold`	0.7	Threshold for relative performance (0-1)
`gpu_individual_perf_threshold`	0.7	Threshold for individual performance (0-1)
`stop_if_detected`	False	Terminate training on straggler
`num_gpu_perf_scores_to_print`	5	Number of best/worst scores to print
`profiling_interval`	1	Profiling interval for detector

Preemption

Plugin (Slurm)

python

from megatron.bridge.recipes.run_plugins import PreemptionPlugin

plugins = [
    PreemptionPlugin(
        preempt_time=60,
        enable_exit_handler=True,
        enable_exit_handler_for_data_loader=False,
    )
]

Plugin parameter	Default	Description
`preempt_time`	60	Seconds before job limit to send signal
`enable_exit_handler`	True	Enable signal handler in training
`enable_exit_handler_for_data_loader`	False	Enable for dataloader workers

Direct config

python

import signal
cfg.train.exit_signal_handler = True
cfg.train.exit_signal = signal.SIGTERM
cfg.train.exit_signal_handler_for_dataloader = False

Re-run state machine (experimental)

python

from megatron.bridge.training.config import RerunStateMachineConfig

cfg.rerun_state_machine = RerunStateMachineConfig(
    rerun_mode="validate_results",
    check_for_nan_in_loss=True,
    check_for_spiky_loss=False,
    spiky_loss_factor=10.0,
)

Parameter	Default	Description
`rerun_mode`	`"disabled"`	`"disabled"`, `"validate_results"`, `"report_determinism_stats"`
`check_for_nan_in_loss`	True	Check for NaN in loss
`check_for_spiky_loss`	False	Check for unexpectedly large loss
`spiky_loss_factor`	10.0	Loss flagged if > factor * max observed (increase for large models)

Exit codes: 16 = resume to disambiguate, 17 = failed validation.

In-process restart (experimental)

python

from megatron.bridge.training.config import InProcessRestartConfig

cfg.inprocess_restart = InProcessRestartConfig(
    enabled=True,
    granularity="node",
    soft_timeout=60.0,
    hard_timeout=90.0,
)

Parameter	Default	Description
`enabled`	False	Enable in-process restart
`active_world_size`	None	Ranks executing workload (rest are warm reserves)
`granularity`	`"node"`	`"node"` or `"rank"` restart granularity
`max_iterations`	None	Max restart attempts (None = unlimited)
`soft_timeout`	60.0	Detect GIL-released hangs (seconds)
`hard_timeout`	90.0	Force-terminate hung ranks (seconds)
`heartbeat_interval`	30.0	Heartbeat interval (seconds)
`heartbeat_timeout`	60.0	Missing heartbeat timeout (seconds)
`barrier_timeout`	120.0	Distributed barrier timeout (seconds)
`completion_timeout`	120.0	Completion barrier timeout (seconds)
`empty_cuda_cache`	True	Clear CUDA cache during restart
`max_rank_faults`	None	Max rank faults before terminating
`monitor_process_logdir`	None	Directory for monitor logs

Required environment variables:

bash

export TORCH_CPP_LOG_LEVEL=error
export TORCH_NCCL_RETHROW_CUDA_ERRORS=0
export NCCL_NVLS_ENABLE=0

The PyTorch NCCL watchdog timeout must exceed hard_timeout. NeMo-Run's Slurm Executor is not supported; launch directly with srun --kill-on-bad-exit=0.

Async checkpoint save

python

cfg.checkpoint.async_save = True
cfg.checkpoint.ckpt_format = "torch_dist"

Local checkpointing (NVRx)

python

cfg.checkpoint.non_persistent_local_ckpt_dir = "/local/scratch/ckpt"
cfg.checkpoint.non_persistent_local_ckpt_algo = "fully_parallel"

Code Anchors

Show full SKILL.md (262 more words)Show less

Fault tolerance

Config: src/megatron/bridge/training/config.py — FaultToleranceConfig
Runtime: src/megatron/bridge/training/fault_tolerance.py
Plugin: src/megatron/bridge/recipes/run_plugins.py — FaultTolerancePlugin
Perf plugin: scripts/performance/nemo-mbridge-resiliency_plugins.py
Tests: tests/unit_tests/training/test_fault_tolerance.py
Example: examples/training_features/nemo-mbridge-resiliency/fault_tolerance/

Straggler detection

Config: src/megatron/bridge/training/config.py — NVRxStragglerDetectionConfig
Runtime: src/megatron/bridge/training/nvrx_straggler.py
Train loop: src/megatron/bridge/training/train.py — check_nvrx_straggler_detection
Tests: tests/unit_tests/training/test_nvrx_straggler.py, tests/functional_tests/training/test_nvrx_straggler.py
Example: examples/training_features/nemo-mbridge-resiliency/straggler_detection/

In-process restart

Config: src/megatron/bridge/training/config.py — InProcessRestartConfig
Runtime: src/megatron/bridge/training/inprocess_restart.py
Entry point: src/megatron/bridge/training/pretrain.py — maybe_wrap_for_inprocess_restart
Tests: tests/unit_tests/training/test_inprocess_restart.py, tests/functional_tests/training/test_inprocess_restart.py

Preemption

Plugin: src/megatron/bridge/recipes/run_plugins.py — PreemptionPlugin
Signal handler: src/megatron/bridge/training/utils/sig_utils.py
Tests: tests/unit_tests/recipes/test_run_plugins.py

Re-run state machine

Config: src/megatron/bridge/training/config.py — RerunStateMachineConfig
Init: src/megatron/bridge/training/initialize.py — init_rerun_state

Checkpointing

Async save: src/megatron/bridge/training/checkpointing.py — schedule_async_save
Local ckpt: src/megatron/bridge/training/checkpointing.py — LocalCheckpointManager
Tests: tests/functional_tests/training/test_local_checkpointing.py

Pitfalls

ft_launcher, not torchrun: Direct FaultToleranceConfig requires ft_launcher. Using torchrun silently disables FT. For non-Slurm, set GROUP_RANK=0.
Async save requires torch_dist: async_save=True only works with ckpt_format="torch_dist". Other formats silently fail or error.
IPR + NeMo-Run: In-process restart is not compatible with NeMo-Run or Slurm preemption plugins. Requires specific PyTorch/NCCL versions and env vars.
NVRx vs legacy straggler: Two detectors exist. Use NVRx (nvrx_straggler); do not enable both.
stop_if_detected default: NVRx logs but does not stop training by default. Set stop_if_detected=True for automatic termination.
NCCL watchdog vs hard_timeout: For IPR, NCCL watchdog timeout must exceed hard_timeout or PyTorch kills the process before recovery.
Rerun state machine is alpha: Use check_for_nan_in_loss=True for NaN detection, but don't rely on full rerun workflows yet.

Verification

Fault tolerance

bash

./examples/training_features/nemo-mbridge-resiliency/fault_tolerance/run_fault_tolerance.sh
./examples/training_features/nemo-mbridge-resiliency/fault_tolerance/run_fault_tolerance.sh --simulate-fault

Look for [FaultTolerance] / [RankMonitorServer] log lines with section timeouts. Simulated fault should trigger restart from checkpoint.

Straggler detection

bash

uv run python -m torch.distributed.run --nproc_per_node=2 \
    examples/training_features/nemo-mbridge-resiliency/straggler_detection/straggler_detection_example.py

Look for GPU relative performance and GPU individual performance reports with per-rank scores.

Async checkpoint

Look for Scheduling async checkpoint save in logs. Training iterations should continue while checkpoint files are being written.

In-process restart

bash

pytest tests/functional_tests/training/test_inprocess_restart.py -v

Requires compatible PyTorch/NCCL versions.

Files

SKILL.md and 5 other files in skills/nemo-mbridge-resiliency of NVIDIA/skills.

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

Open the folder on GitHubat commit dfdd080

Compare with similar skills

Nemo Mbridge Resiliency 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.

Nemo Mbridge Resiliency compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Nemo Mbridge Resiliency this skillNVIDIA/skills	3.5k	—	~2.8k	Automated safety check: Pass	Apache-2.0	today
LLM Torch Profiler Analysissgl-project/sglang	37k	2 repos	~6.4k	Automated safety check: Pass	Apache-2.0	today
Skill InspectorNVIDIA/SkillSpector	20k	—	~1.8k	Automated safety check: Pass	Apache-2.0	today
Megatron-LM Container and Dependency SetupNVIDIA/Megatron-LM	18k	—	~2.6k	Automated safety check: Pass	Apache-2.0	today
Embeddings via 9Routerdecolua/9router	30k	—	~604	Automated safety check: Pass	MIT	today
Megatron-LM Base Image BumpNVIDIA/Megatron-LM	18k	—	~2.8k	Automated safety check: Pass	Apache-2.0	today

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NVIDIA/skills
Official
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
3.5k GitHub starsUsed in 1 repo~4.5k tokens
Auto-check passed
HSB IP Def File Tool
NVIDIA/skills
Official
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
3.5k GitHub stars~2.9k tokensUpdated today
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Isaac Sim Mission Control Showcase
NVIDIA/skills
Official
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
3.5k GitHub stars~4.8k tokensUpdated today
Auto-check passed
Defect Image Generation with Cosmos AnomalyGen
NVIDIA/skills
Official
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
3.5k GitHub stars~5k tokensUpdated today
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Physical AI Video Augmentation on OSMO
NVIDIA/skills
Official
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
3.5k GitHub stars~4.7k tokensUpdated today
Auto-check: notes
TAO Detection KPI Analysis
NVIDIA/skills
Official
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
3.5k GitHub stars~2.7k tokensUpdated today
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Works with

NVIDIA AI Platform

Questions about Nemo Mbridge Resiliency

What does Nemo Mbridge Resiliency do?

Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine. Nemo Mbridge Resiliency is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

How do I install Nemo Mbridge Resiliency in Claude Code?

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

How do I install Nemo Mbridge Resiliency in Codex?

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

Can I use Nemo Mbridge Resiliency 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-resiliency -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-resiliency, .gemini/skills/nemo-mbridge-resiliency, .github/skills/nemo-mbridge-resiliency and .opencode/skills/nemo-mbridge-resiliency in your project.

What does Nemo Mbridge Resiliency need to run?

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

Does Nemo Mbridge Resiliency 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 Resiliency 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 Resiliency use?

Nemo Mbridge Resiliency 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 Resiliency use?

About 2.8k tokens (SKILL.md is roughly 11k 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 Resiliency?

Skills that share tags, products or a category with Nemo Mbridge Resiliency: LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Skill Inspector (NVIDIA/SkillSpector, 20k stars), Megatron-LM Container and Dependency Setup (NVIDIA/Megatron-LM, 18k stars) and Embeddings via 9Router (decolua/9router, 30k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemo Mbridge Resiliency?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 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 Resiliency

Facts

At a glance

What it does

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Resiliency

Enablement

Fault tolerance (Slurm only)

Option 1: NeMo Run plugin (recommended)

Option 2: Direct config + ft_launcher

NVRx straggler detection

Preemption

Plugin (Slurm)

Direct config

Re-run state machine (experimental)

In-process restart (experimental)

Async checkpoint save

Local checkpointing (NVRx)

Code Anchors

Fault tolerance

Straggler detection

In-process restart

Preemption

Re-run state machine

Checkpointing

Pitfalls

Verification

Fault tolerance

Straggler detection

Async checkpoint

In-process restart

Files

Compare with similar skills

Similar skills

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Related

Questions about Nemo Mbridge Resiliency