AD smyx-thermal-fever-screening-analysis
Using a fixed thermal-imaging camera installed in public areas (e.g., living room, dining room), the system automatically analyzes each person's skin-surface temperature (usually forehead or facial region) when multiple people gather, and computes the difference between an individual's temperature and the average temperature of others in the scene. | 通过安装于公共区域（如客厅、餐厅）的固定热成像摄像头，在多人聚集时自动分析每个人的体表温度（通常为额头或面部区域），计算个体温度与场景内其他人平均温度的差值。当某个人温度显著高于周边人群（差值超过预设阈值，如1.5℃）时，输出'体温相对异常'提醒，建议使用额温枪复测。
As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
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 · 0
✓ No critical or high findings
Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 38/100
- 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
- 20When it triggers. No condition that starts the skill
- 25Steps. 1 steps
- 30Running it twice. 1 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1663 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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -255 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 487: enough signal without eating the budget
- +4Structure: 19 headings
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.