BF smyx-fish-feeding-activity-analysis
Through built-in cameras of smart feeders or fixed cameras on aquariums, the system captures fish feeding videos after feeding. Using AI object detection and motion analysis, it identifies the number of fish gathering for food, feeding intensity (fish swimming speed, feeding action frequency), and remaining feed amount, and computes a comprehensive feeding activity score (0-100). | 通过智能喂食器内置摄像头或鱼缸固定摄像头，在投喂后拍摄鱼群摄食视频，利用 AI 目标检测和运动分析技术，识别鱼群聚集抢食的数量、摄食强度（鱼只游动速度、摄食动作频率）以及剩余饲料量，综合计算摄食活跃度评分（0-100 分）。当活跃度评分低于阈值时，输出'食欲下降'提示，可能预示疾病、水质恶化或应激反应。
Through built-in cameras of smart feeders or fixed cameras on aquariums, the system captures fish feeding videos after feeding.
As a process F 32/100 · 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 32/100
- 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
- 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 2139 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
- -268 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 537: 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.