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AF physicsnemo-shard-tensor

Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops, and bootstrap multi-GPU correctness tests. Use when working with ShardTensor, scatter_tensor, domain parallelism, sequence/spatial sharding, ring attention, DeviceMesh + DDP/FSDP2 hybrid parallelism, or physicsnemo.domain_parallel. Do NOT use for generic PyTorch DDP/FSDP setup without domain parallelism, picking a PhysicsNeMo model or example (use physicsnemo-discover), or non-distributed training questions.

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 3 232 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or…

As a process F 46/100 · Will not run — References files that are not bundled: examples/weather/stormcast/utils/parallel.py, examples/minimal/ShardTensorExamples/5_vit_training_loop/, references/new-op-patterns.md

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
46/100
Will not run
References files that are not bundled: examples/weather/stormcast/utils/parallel.py, examples/minimal/ShardTensorExamples/5_vit_training_loop/, references/new-op-patterns.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: examples/weather/stormcast/utils/parallel.py
  • warning missing-ref reference to a missing file: examples/minimal/ShardTensorExamples/5_vit_training_loop/
  • warning missing-ref reference to a missing file: references/new-op-patterns.md
  • warning missing-ref reference to a missing file: references/testing.md
  • warning missing-ref reference to a missing file: references/integration-checklist.md

Process rating: all ten parameters 46/100

Will not run. References files that are not bundled: examples/weather/stormcast/utils/parallel.py, examples/minimal/ShardTensorExamples/5_vit_training_loop/, references/new-op-patterns.md
  • 0Tools and files. 5 referenced file(s) missing: examples/weather/stormcast/utils/parallel.py, examples/minimal/ShardTensorExamples/5_vit_training_loop/, references/new-op-patterns.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 27 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3232 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 651: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (3 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.