SKILLEMALL.ai

BD qzcli

Manage GPU compute jobs on the Qizhi (启智) platform using qzcli — a kubectl-style CLI tool. Use when user says "qzcli", "启智平台", "submit job", "stop job", "查计算组", "avail", "list jobs", "batch submit", or needs to manage distributed training jobs on a Qizhi instance.

wanshuiyin/Auto-claude-code-research-in-sleep Claude Code author: wanshuiyin MIT 1 file body ≈ 1 864 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Manage GPU compute jobs on the Qizhi (启智) platform using qzcli — a kubectl-style CLI tool.

As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

IntegrationKubernetesGitHubSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
86
Run on models
none yet
Process rating
D
41/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: qzcli (wanshuiyin/Auto-claude-code-research-in-sleep)

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

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash(*)
      allowed-tools: Bash(*) Read Write

    Files scanned: 1. 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 41/100

    • 0Steps. Prose only: no discrete steps
    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. Tools declared in frontmatter
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1864 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 264: enough signal without eating the budget
    • +4Structure: 18 headings
    • +4Has examples (16 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.