BF smyx-reptile-excrement-analysis-analysis
Through a fixed camera in the reptile enclosure, the system captures a high-definition image (or a static video frame) once excrement is found, and uses AI visual analysis to identify urate (white/milky-white crystals or paste, common in lizards, geckos, etc.) — including its size (pixel area) — and to identify the morphology of feces (normally formed log, soft pasty, watery, or bloody). | 通过爬宠箱固定摄像头，在发现排泄物后拍摄高清图像（或分析视频中的静态帧），利用 AI 视觉分析技术识别尿酸（白色/乳白色结晶或膏状物，常见于蜥蜴、守宫等爬宠）的大小（面积像素）以及粪便的形态（正常成形条状、稀软糊状、水样或带血）。
Through a fixed camera in the reptile enclosure, the system captures a high-definition image (or a static video frame) once excrement is found, and uses AI…
As a process F 34/100 · Will not run — 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 34/100
- 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
- 20When it triggers. No condition that starts the skill
- 21Steps. 1 steps, 2 vague phrases
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2669 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
- +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
- -280 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 508: 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.