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Run explicitly requested ModelOpt Quantization-Aware Distillation (QAD) on Slurm through Megatron Bridge to recover a measured BF16-to-PTQ accuracy gap. Use only when the user explicitly asks for QAD, including its topology, data preparation, Slurm launch, resume, checkpoint export, or recovery decisions.

NVIDIA/Model-Optimizer Agent Skills author: NVIDIA Apache-2.0 1 file body ≈ 1 296 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Run explicitly requested ModelOpt Quantization-Aware Distillation (QAD) on Slurm through Megatron Bridge to recover a measured BF16-to-PTQ accuracy gap.

As a process D 39/100 · Unfinished process — References files that are not bundled: examples/megatron_bridge/README.md, examples/megatron_bridge/{quantize.py,distill.py}, examples/megatron_bridge/data/nemotron-cascade-2-blend.yaml

ProcedureData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
D
39/100
Unfinished process
References files that are not bundled: examples/megatron_bridge/README.md, examples/megatron_bridge/{quantize.py,distill.py}, examples/megatron_bridge/data/nemotron-cascade-2-blend.yaml
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/megatron_bridge/README.md
  • warning missing-ref reference to a missing file: examples/megatron_bridge/{quantize.py,distill.py}
  • warning missing-ref reference to a missing file: examples/megatron_bridge/data/nemotron-cascade-2-blend.yaml

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: examples/megatron_bridge/README.md, examples/megatron_bridge/{quantize.py,distill.py}, examples/megatron_bridge/data/nemotron-cascade-2-blend.yaml
  • 0Tools and files. 3 referenced file(s) missing: examples/megatron_bridge/README.md, examples/megatron_bridge/{quantize.py,distill.py}, examples/megatron_bridge/data/nemotron-cascade-2-blend.yaml
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1296 tokens

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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 306: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 19 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.