BD smyx-plant-nutrient-diagnosis-analysis
AI-powered plant nutrient deficiency diagnosis from leaf images. Detects leaf color, morphology changes (pale green/yellow-green/purple-red, marginal scorch, interveinal chlorosis) via computer vision, matches against common deficiency symptom databases, and outputs the most likely deficient nutrient element (nitrogen, phosphorus, potassium, iron, magnesium, zinc, etc.) with confidence score. Enables precision fertilization, avoids blind over-fertilization. Scenarios: smart planters, home gardening, agricultural greenhouses, plant factories. | 通过智能花盆、农业大棚或手机拍摄的植物叶片高清图像，利用AI视觉分析技术识别叶片颜色、形态变化（如叶色浅绿/黄绿/紫红、叶缘焦枯、叶脉间失绿等），与常见营养缺乏症特征库比对，输出最可能缺乏的营养元素（氮、磷、钾、铁、镁、锌等）及置信度。有助于精准施肥，避免盲目用肥造成浪费或伤害。应用场景：智能花盆、家庭园艺、农业大棚、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 1982 tokens
- 100Running it twice. No mutating operations
- low 14 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
- -291 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 715: enough signal without eating the budget
- +4Structure: 24 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.