AB skin-analysis-expert
Analyze a person's skin from a single selfie using YouCam (Perfect Corp) AI. Returns 16 skin-condition scores plus skin type, as a readable report. Use when the user wants a skin analysis / skin report / skin score — incl. "幫我看膚質", "膚況分析", "how's my skin", "skin report". Do NOT use for skin-tone/facial attributes (that's facial-consultant), makeup, or hair.
Analyze a person's skin from a single selfie using YouCam (Perfect Corp) AI.
As a process B 76/100 · Nearly there — weak spots: consistency, running it twice
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:34High-entropy token-like string (may be an id, hash or a credential)example: pfNK…Hjv+KoBIxbE=
Files scanned: 12. 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 76/100
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (skin-analysis-expert) differs from the folder (youcam-skin-analysis-expert)
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 10 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 1184 tokens
- 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
- -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 359: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 10 items
- +3Output format is stated explicitly
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.