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AF cupynumeric-parallel-data-load

Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 4 321 tokens Open the sourceclawhub.ai↗ analyzed 10 h ago

Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual…

As a process F 34/100 · Will not run — References files that are not bundled: assets/examples/parallel_npy_load.py, examples/dlpack/leaf_task_interop.py

GeneratorSoftware developmentData and analyticstype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
34/100
Will not run
References files that are not bundled: assets/examples/parallel_npy_load.py, examples/dlpack/leaf_task_interop.py
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: assets/examples/parallel_npy_load.py
  • warning missing-ref reference to a missing file: examples/dlpack/leaf_task_interop.py

Process rating: all ten parameters 34/100

Will not run. References files that are not bundled: assets/examples/parallel_npy_load.py, examples/dlpack/leaf_task_interop.py
  • 0Tools and files. 2 referenced file(s) missing: assets/examples/parallel_npy_load.py, examples/dlpack/leaf_task_interop.py
  • 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
  • 30Running it twice. 5 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 4321 tokens
  • 85Steps. 9 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress

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
  • +2Single-language instructions
  • +3Description length 375: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (11 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.