mcpbeat

Nvrx Attr

nvidia/nvrx-attr

> Orchestration layer over nvidia_resiliency_ext attribution modules. Provides log-analysis, fr-analysis, and a Megatron-LM-oriented fault-injection feedback loop for benchmarking attribution quality on SLURM workloads.

27k tokens
context cost
the whole folder, loaded on every use
15
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
319
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/NVIDIA/nvidia-resiliency-ext --skill nvrx-attr

What comes with it

75 090 bytes besides the instruction
fr-analysis/scripts/fr_attribution.py
log-analysis/scripts/nvrx_logsage.py
scripts/l4_gb200_reduced.sh
scripts/n3_super_gb200_fi.sh
scripts/pools/n3_super_8node.txt
scripts/prepare_node_alloc.sh
scripts/run_session.sh
scripts/score_attribution.py
scripts/user.env.example
scripts/watch_and_analyze.sh
scripts/workloads.conf

The instruction itself

5 sections, as written by the author

Attribution Skills

High-level orchestration layer over the nvidia_resiliency_ext.attribution modules.

Each subdirectory is a self-contained skill with its own SKILL.md and helper scripts.

Skills

| Directory | Purpose | Entry point |

|-----------|---------|------------|

| log-analysis/ | Analyze SLURM job logs for failure root-cause and restart decisions | NVRxLogAnalyzer (nvrx_logsage.py) |

| fr-analysis/ | Analyze NCCL flight-recorder dumps for collective-hang root-cause | CollectiveAnalyzer (fr_attribution.py) |

| fault-injection-loop/ | Run a batched SLURM fault-injection feedback loop and score attribution accuracy | prepare_node_alloc.sh / watch_and_analyze.sh |

How skills relate to the library

src/nvidia_resiliency_ext/
├── attribution/
│   ├── log_analyzer/nvrx_logsage.py      ← log-analysis implementation
│   ├── trace_analyzer/fr_attribution.py  ← fr-analysis implementation
│   ├── analyzer/engine.py                ← combined orchestration entry point
│   └── combined_log_fr/                  ← optional log + FR fusion
└── skills/
    └── nvrx-attr/                        ← this skill bundle
        ├── log-analysis/
        ├── fr-analysis/
        └── fault-injection-loop/

The Analyzer (analyzer/engine.py) is the recommended entry point when you need

request coalescing, result caching, or the combined LOG_AND_TRACE pipeline.

Use the individual skills when you want to run one analysis type directly without the

full coalescing stack.

Common prerequisites

  • LLM_API_KEY environment variable, LLM_API_KEY_FILE, or ~/.llm_api_key
  • langchain-openai installed
  • logsage package installed (required by log_analysis)
  • Package installed: pip install nvidia-resiliency-ext or pip install -e . from repo root
  • The fault-injection loop has only been validated with Megatron-LM training scripts

Fault-Loop Local Setup

Before using fault-injection-loop/, create the local config file from the tracked

template and fill in your site-specific values:

cp scripts/user.env.example scripts/user.env

The feedback-loop scripts require src/nvidia_resiliency_ext/skills/nvrx-attr/scripts/user.env

to exist at runtime. Keep user.env local and untracked.

How to use it

Copy the folder

Take nvidia/nvrx-attr from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.