BF smyx-hydroponic-nutrient-assessment-analysis
Using fixed cameras on a hydroponic growing rack to capture high-resolution images of plant roots (in transparent containers) and leaves (young and old), AI vision analysis identifies root color (white = healthy, yellow = early stress, brown = severe stress, black = rotting) and leaf morphology (tip burn, leaf-margin scorch, yellowing, curling) to judge whether the nutrient solution is too concentrated or too dilute, and. | 通过水培种植架的固定摄像头拍摄植物根系（透明容器）和叶片（新叶、老叶）的高清图像，利用AI视觉分析技术识别根须颜色（白色健康、黄色初期胁迫、褐色严重胁迫、黑色腐烂）、叶片形态（叶尖灼伤、叶缘焦枯、叶片黄化、卷曲）等特征，判断营养液浓度是否过浓或过稀，并输出调整建议（增加清水稀释或补充浓缩营养液）。
As a process F 21/100 · Will not run — References files that are not bundled: scripts/smyx_hydroponic_nutrient_concentration_assessment_analysis.py
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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") - warning
missing-refreference to a missing file: scripts/smyx_hydroponic_nutrient_concentration_assessment_analysis.py
Process rating: all ten parameters 21/100
- 0Tools and files. 1 referenced file(s) missing: scripts/smyx_hydroponic_nutrient_concentration_assessment_analysis.py
- 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
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1627 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
- -254 emoji in the instructions: noise for the model
- -33 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 577: 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: 63.