BD smyx-indoor-plant-light-stress-detect-analysis
AI-powered indoor plant light stress detection from smart planter or fixed camera images (optionally combined with light sensor lux data). Detects morphological anomalies caused by low light (elongated internodes / etiolation, thin leaves, pale green color) or strong-light damage (leaf burn spots, scorched edges, curling, bleaching). Combined with optional lux sensor readings, it determines the current light stress type (insufficient / excessive / normal) and outputs adjustment suggestions (e.g. move to window, add shading, adjust grow-light duration). Scenarios: smart planters, indoor green plant care, home gardening, office plants. | 通过智能花盆或固定摄像头拍摄植物整体图像（也可选配光照传感器数据），利用AI视觉分析技术检测植物因光照不足引起的形态异常（如茎节间距拉长—徒长、叶片变薄、颜色浅绿）或因光照过强引起的损伤（叶片灼伤斑、焦边、卷曲、褪绿）。结合可选的光照传感器实时数据（勒克斯值），综合判断植物当前所受的光照胁迫类型（不足/过强/正常），并输出光照调整建议（如"增加光照，可移至窗边""遮阴，避免直射光"）。应用场景：智能花盆、室内绿植养护、家庭园艺、办公室植物。
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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Unexpected scalar at node end at line 2, column 830: …合判断植物当前所受的光照胁迫类型（不足/过强/正常），并输出光照调整建议（如"增加光照，可移至窗边""遮阴，避免直射光"）。应用场景：智能花盆、室内绿植养护… ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - 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 1425 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 866: 120–800 characters recommended
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -255 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +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: 61.