BD smyx-plant-wilting-quantification-analysis
AI-powered plant wilting quantification from full-plant images via smart pots or fixed cameras. Detects leaf-stem angle (leaf droop), stem straightness, and leaf turgidity to quantify wilting severity (0-100%). Optionally fuses soil-moisture sensor data to discriminate dehydration (underwatering) vs. waterlogging (root hypoxia), and auto-triggers watering or drainage prompts for precision irrigation. Scenarios: smart pots, home gardening, greenhouses, plant factories. | 通过智能花盆或固定摄像头拍摄植物整体图像，利用AI视觉分析技术检测叶片与茎秆的夹角（叶片下垂角度）、茎秆挺直程度以及叶片舒展度，量化萎蔫程度（0-100%）。可选结合土壤湿度传感器数据，综合判断萎蔫原因是缺水还是水涝（根部缺氧导致）。可自动触发灌溉或排水提示，帮助用户精准浇水。应用场景：智能花盆、家庭园艺、温室大棚、植物工厂。
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 1613 tokens
- 100Running it twice. No mutating operations
- low 13 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
- -270 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 639: enough signal without eating the budget
- +4Structure: 23 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.