BD smyx-aquarium-plant-health-monitor-analysis
AI-powered aquatic plant health monitoring from aquarium camera images. Detects leaf color changes (yellowing, bleaching, blackening), morphological anomalies (melting, curling, holes), and iron deficiency symptoms in submerged plants. Outputs health assessment and care suggestions (e.g., supplement iron fertilizer, adjust lighting, increase CO₂). Helps early detection of aquatic plant issues and maintains aquarium ecological balance. Scenarios: smart fish tanks, aquascaping tanks, aquarium shops. | 通过智能鱼缸或水下摄像头拍摄水草的图像，利用AI视觉分析技术识别水草叶片的颜色变化（黄化、白化、发黑）、形态异常（溶叶、卷曲、穿孔）以及缺铁等典型症状，输出健康评估及养护建议（如补充铁肥、调整光照、增加CO₂）。有助于及早发现水草生长问题，维持水族箱生态平衡。应用场景：智能鱼缸、水族箱、水草造景缸、水族店。
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 1964 tokens
- 100Running it twice. No mutating operations
- low 15 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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -293 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 659: enough signal without eating the budget
- +4Structure: 25 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.