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AB tensorpool

This skill helps users migrate their local machine learning scripts to run on TensorPool GPU clusters using the interactive cluster workflow (tp ssh). Use this when a user has a working local script and wants to scale it up to professional GPU hardware.

ClawHub Agent Skills author: Tycho-Svoboda v1.0.0 MIT-0 2 files body ≈ 2 825 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

As a process B 68/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureInfrastructuretype 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
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration read-dotenv SKILL.md:246
      Reads a .env file
      export $(cat .env | xargs)
    • low Dangerous commands cmd-background-process SKILL.md:284
      Starts a background / autostarted process
      nohup python train.py > training.log 2>&1 &

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 11 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 60 steps, 3 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2825 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • high The skill tells the model to perform an irreversible action with no human approval

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 253: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 60 items
    • +4Has examples (20 code blocks)

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

    External checks

    ClawHub: suspicious
    This TensorPool skill is mostly aligned with cloud GPU migration, but it needs review because it can guide paid cluster use, remote code transfer, code edits, and raw secret-file handling without enough explicit user control.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026