BD smyx-reptile-shedding-progress-analysis
Through a fixed camera in the reptile enclosure, the system periodically captures full-body high-definition images of reptiles (snakes, lizards, geckos) and uses AI visual analysis to detect changes in body colour (normal vivid → dull/whitish → restored vivid) and eye state (clear → opaque milky 'blue-phase' → clear again), to determine the shedding phase: preparation phase (skin turns whitish, eyes turn opaque), in-progress. | 通过爬宠箱固定摄像头，定期拍摄爬行动物（如蛇、蜥蜴、守宫）的全身高清图像，利用 AI 视觉分析技术检测体表颜色变化（正常体色 → 发白/灰白 → 恢复鲜艳）以及眼部状态（透明 → 浑浊灰白 → 再次透明），判断蜕皮阶段：准备期（皮肤发白、眼睛浑浊）、进行期（头部或局部开始蜕皮）、完成期（旧皮完全脱离，体色恢复）。系统每日或每半日自动分析，输出蜕皮阶段及护理建议。
Through a fixed camera in the reptile enclosure, the system periodically captures full-body high-definition images of reptiles (snakes, lizards, geckos) and…
As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
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 · 0
✓ No critical or high findings
Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 35/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2771 tokens
- 100Running it twice. No mutating operations
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
- -286 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 614: 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: 69.