SKILLEMALL.ai

BD qinghu-model-outfit-restore

青虎AI 模特换装高一致性还原:上传模特图与要替换的衣物图,一键完成精准换装,保持人物姿态与光影高度一致、衣物细节还原到位,适配电商穿搭快速出图。当用户要给模特换衣服、换装、试穿、把这件衣服穿到模特身上、做穿搭图时必须触发。关键词:青虎AI、模特换装、换衣服、虚拟试穿、一致性还原、姿态保持、光影一致、穿搭图、电商出图。

ClawHub Agent Skills author: AutoAGC v0.1.5 MIT-0 2 files body ≈ 1 202 tokens Open the sourceclawhub.ai analyzed 2 d ago

青虎AI 模特换装高一致性还原:上传模特图与要替换的衣物图,一键完成精准换装,保持人物姿态与光影高度一致、衣物细节还原到位,适配电商穿搭快速出图。当用户要给模特换衣服、换装、试穿、把这件衣服穿到模特身上、做穿搭图时必须触发。关键词:青虎AI、模特换装、换衣服、虚拟试穿、一致性还原、姿态保持、光影一致、穿搭图、电商出图。

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticsCommerceMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
43/100
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 · 0

✓ No critical or high findings

Files scanned: 2. 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 "homepage"

Process rating: all ten parameters 43/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 100Steps. 28 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1202 tokens

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 160: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This skill appears to do the advertised virtual try-on workflow, but it asks agents to install and run a mutable third-party CLI and handle user images and service tokens with limited safeguards.
LLM: suspicious (medium) · 8 Sept 2026