SKILLEMALL.ai

AC alibabacloud-pts-pilot

Router skill for Alibaba Cloud PTS (Performance Testing Service) operations. Resolves the user's PTS intent and delegates execution to the appropriate sub-skill. Itself does NOT execute any CLI / API / business logic. Triggers "PTS", "压测", "性能测试", "stress testing", "performance testing", "JMeter", "load testing", "压测调优".

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 3 files body ≈ 4 101 tokens Open the sourceclawhub.ai analyzed 2 d ago

Router skill for Alibaba Cloud PTS (Performance Testing Service) operations.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 13 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Steps. 29 steps, 4 vague phrases
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4101 tokens
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3Output format is not stated: the model decides each time
  • -220 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 322: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
The skill appears to be a cloud-performance-testing router, but it can silently choose a cloud region and delegate state-changing operations without clear user confirmation.
LLM: suspicious (medium) · VirusTotal: · 23 Jun 2026