SKILLEMALL.ai
GitHub ↗ RU Sign in

AC ecommerce-img-gen

跨境电商图片生成工具。根据产品图生成各平台（Amazon / Shopee / TikTok Shop / Lazada / AliExpress / Temu / SHEIN）主图、详情页长图、生活场景图。内置6种视觉风格路由、6层合规审查（意图/品牌/版权/文化/平台/授权）。支持模型切换：nano-banana-2（快速，默认2K）和 nano-banana-pro（高质量，支持4K终稿）。触发场景：(1) 用户发产品图要求"做电商图"、"生成主图"、"详情页"；(2) 用户提到具体平台+主图或详情页；(3) 用户要求"全套图"、"多平台图"；(4) 用户发产品图后指定品牌和目标市场。

ClawHub Agent Skills author: howtimeschange v2.6.0 MIT-0 9 files body ≈ 960 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMarketingInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
C
50/100
Has gaps
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 · 0

✓ No critical or high findings

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")
  • note frontmatter-key unknown frontmatter key "title"

Process rating: all ten parameters 50/100

  • 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
  • 75Steps. 3 steps
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 960 tokens
  • 100Running it twice. No mutating operations
  • low 12 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 299: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 3 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (6 of 6)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This is a real ecommerce image-generation skill, but it needs Review because it under-discloses local credential fallback behavior while sending prompts and product images to 1xm.ai.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026