AB hair-diagnostics
Diagnose hair-health metrics — density, type, frizziness, length — from a single selfie using YouCam (Perfect Corp) AI, and return one combined report. Use for "頭髮體檢", "髮質分析", "hair diagnostics", "hair health report". Do NOT use for hairstyle/color try-on (that's hair-color-style-advisor), skin, makeup, or clothes.
Diagnose hair-health metrics — density, type, frizziness, length — from a single selfie using YouCam (Perfect Corp) AI, and return one combined report.
As a process B 75/100 · 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 · 4
✓ No critical or high findings
Medium and low: 4
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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=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:70High-entropy token-like string (may be an id, hash or a credential)- pfNK…Hjv+KoBIxbE=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:110High-entropy token-like string (may be an id, hash or a credential)- pfNK…Hjv+KoBIxbE=
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low Secrets in code
secret-high-entropy-tokenscripts/api-fallback.yaml:140High-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 75/100
- 40Consistency. Frontmatter name (hair-diagnostics) differs from the folder (youcam-hair-diagnostics)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 16 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 719 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
- -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: 7 headings
- +3Step-by-step instructions: 16 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.