BD smyx-lawn-health-assessment-analysis
AI-powered lawn health assessment from drone or fixed-camera top-down images. Uses semantic segmentation to distinguish healthy turf (green), wilting/yellow turf (yellow-brown), bare soil and weeds (off-species color/texture), then computes wilting area ratio and weed coverage ratio, and outputs a composite lawn health score (0-100). Helps managers of golf courses, courtyards or municipal greenways quantify turf quality and guide irrigation, fertilization and weeding operations. Scenarios: home courtyards, golf courses, municipal park lawns, sports fields. | 通过无人机或固定摄像头拍摄草坪的俯视图像，利用AI语义分割技术区分健康草坪（绿色）、枯黄草坪（黄/褐色）、裸土以及杂草（非目标草种，颜色和纹理不同），计算枯黄面积占比和杂草覆盖面积占比，综合评估草坪健康评分（0-100分）。该技能有助于高尔夫球场、庭院或市政绿地管理者量化草坪质量，指导灌溉、施肥及除草作业。应用场景：家庭庭院、高尔夫球场、市政公园草坪、运动场。
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 1405 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
- -256 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 746: 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.