SKILLEMALL.ai

AD ecommerce-image-suite

电商套图生成助手。用户明确提出需要生成电商套图、商品主图、卖点图、场景图、模特图等图片内容时触发。 支持国内平台(淘宝、京东、拼多多、抖音)与国际跨境平台(Amazon、独立站)的尺寸规范。 触发示例:「帮我生成这件T恤的电商套图」「做一套淘宝主图」「生成亚马逊listing图片」。 不应在用户仅上传图片但未明确提出图片生成需求时触发。

ClawHub Agent Skills author: 🍡 v1.0.6 MIT-0 9 files body ≈ 1 890 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
79
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration exfil-secret-in-url references/providers.md:51
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    `https://generativelanguage.googleapis.com/v1beta/models/imag…ict?key=…
    placeholder

Files scanned: 9. 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 46/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1890 tokens
  • 100Running it twice. No mutating operations
  • 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

  • +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 169: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 3 scripts are documented

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

External checks

ClawHub: suspicious
This skill appears to be a legitimate ecommerce image generator, but it needs review because it can send API keys and product prompts to arbitrary custom proxy URLs and partially prints key prefixes.
LLM: suspicious (high) · VirusTotal: · 29 May 2026