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The data-mover for TAO jobs — decides the storage tier (A pre-positioned mount with zero fetch / B volume-from-S3 / C ephemeral in-compute fetch), stages inputs (bulk + annotation-selective + archive extract + HF/NGC PTM), maps credentials to env, routes outputs 3-way with upload-excludes, and runs the compute-frame verify gate. A support skill other platform skills (docker, kubernetes, slurm, brev, virtualenv) call to get data to and from the compute container without the TAO SDK. Trigger phrases include "stage inputs", "mount the dataset", "upload TAO results", "download only referenced files", "resolve results_dir", "verify the container can read the data".

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 1 214 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

The data-mover for TAO jobs — decides the storage tier (A pre-positioned mount with zero fetch / B volume-from-S3 / C ephemeral in-compute fetch), stages…

As a process C 55/100 · Has gaps — References files that are not bundled: references/selective_download.py, references/tests/

IntegrationKubernetesDockerAWSInfrastructureData 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
C
89/100
safety, quality, tests
Safety 60%
93
Quality 40%
84
Run on models
none yet
Process rating
C
55/100
Has gaps
References files that are not bundled: references/selective_download.py, references/tests/
Tools and files w 18
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Bash
  • low Exfiltration read-dotenv SKILL.md:42
    Reads a .env file
    set -a; source /path/to/.env; set +a   # omit if already exported
  • low Exfiltration read-dotenv SKILL.md:98
    Reads a .env file
    set -a; source /path/to/.env; set +a   # omit if already exported

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: references/selective_download.py
  • warning missing-ref reference to a missing file: references/tests/
  • note edit-residue the text marks something as outdated (lines 33): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 55/100

Will not run. References files that are not bundled: references/selective_download.py, references/tests/
  • 0Tools and files. 2 referenced file(s) missing: references/selective_download.py, references/tests/
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 12 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1214 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 668: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 12 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.