AB clothes-tryon-studio
Virtual clothes try-on studio using YouCam (Perfect Corp) AI. Swap outfits onto the user's photo, optionally change the background, and optionally turn the result into a short motion video (turn / runway / pose). Use for "換衣", "虛擬試穿", "outfit try-on", "try on clothes". Do NOT use for makeup, hair, or skin analysis.
Virtual clothes try-on studio using YouCam (Perfect Corp) AI.
As a process B 74/100 · Nearly there — weak spots: consistency
How to improve
- 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 · 5
✓ No critical or high findings
Medium and low: 5
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:55High-entropy token-like string (may be an id, hash or a credential)example: pfNK…Hjv+KoBIxbE=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:76High-entropy token-like string (may be an id, hash or a credential)example: pfNK…Hjv+KoBIxbE=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:101High-entropy token-like string (may be an id, hash or a credential)example: pfNK…Hjv+KoBIxbE=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:105High-entropy token-like string (may be an id, hash or a credential)example: pfNK…Hjv+KoBIxbE=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:167High-entropy token-like string (may be an id, hash or a credential)example: pfNK…Hjv+KoBIxbE=
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "requirements"
Process rating: all ten parameters 74/100
- 40Consistency. Frontmatter name (clothes-tryon-studio) differs from the folder (youcam-clothes-tryon-studio)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 15 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 716 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- -34 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +4Description says when NOT to use the skill
- +3Description length 316: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 15 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.