BC 护肤小助手
Professional skincare advisor. Triggers on: skincare advice, product recommendations, sensitive skin care, anti-aging, acne control, moisturizing, sunscreen, personalized routine. Covers: barrier repair, hydration, anti-aging, oil control, sun protection.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
-
medium Broad scope
meta-agent-memory-dump.workbuddy/memory/MEMORY.mdAgent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokens.workbuddy/memory/MEMORY.md
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "trigger_conditions"
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (护肤小助手) differs from the folder (ai-skincare-assistant)
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 6 branches
- 100Tools and files. No external tools needed
- 100Steps. 12 steps
- 100Execution cost. Instruction body is 3873 tokens
- low 10 top-level sections: this looks like several domains in one skill
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
- +4No input/output examples
- -232 emoji in the instructions: noise for the model
- -43 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 255: enough signal without eating the budget
- +4Structure: 37 headings
- +3Step-by-step instructions: 12 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.