SKILLEMALL.ai

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帮助SHEIN卖家、服装跨境独立站通过青虎AI完成“SHEIN商品图”:支持生成时尚模特穿搭图、服装平铺图、细节图、欧美流行风场景图、多角度展示图。可调用GPT Image 2、Nano Banana 2 等大模型,结合欧美时尚圈的流行元素和高清质感,快速打造符合快时尚审美的商品图。适用于SHEIN、SHEIN快时尚独立站等服装类目爆款打造。Use this skill for SHEIN商品图, 时尚穿搭, 模特图, 快时尚, 欧美风格, 流行元素, GPT Image 2, Nano Banana 2 , AIGC图像生成, 服装电商。通过青虎AI统一接入,支持素材上传、实时模型配置、任务轮询和结果下载。 当用户要求做SHEIN 商品图、主图套图、详情图、活动图生成或该平台的主图/套图/详情图/活动图时必须触发。关键词:LinkPix、qhkit、青虎、SHEIN商品图、时尚穿搭、模特图、平铺图、快时尚、欧美风格、服装跨境。

ClawHub Agent Skills author: AutoAGC v0.1.1 MIT-0 2 files body ≈ 1 530 tokens Open the sourceclawhub.ai analyzed 3 d ago

帮助SHEIN卖家、服装跨境独立站通过青虎AI完成“SHEIN商品图”:支持生成时尚模特穿搭图、服装平铺图、细节图、欧美流行风场景图、多角度展示图。可调用GPT Image 2、Nano Banana 2…

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

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
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

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    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, python, node) that frontmatter does not declare
    • 100Steps. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1530 tokens
    • 100Running it twice. No mutating operations

    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 420: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    The skill’s image-generation purpose is clear, but it asks the agent to install unpinned executable packages and handle an API key in ways users should review carefully.
    LLM: suspicious (high) · 8 Sept 2026