BD smyx-transpiration-rate-estimation-analysis
AI-powered transpiration rate estimation for indoor plants. From smart planters or fixed cameras, uses thermal infrared images of leaves (preferred) — or regular RGB images combined with ambient temperature/humidity — to estimate the leaf-to-air temperature difference, combines radiation/humidity parameters (sensor or model-inferred), and computes a relative transpiration rate index (0-100%). Transpiration rate correlates with root water-uptake activity, indirectly reflecting root health and water transport capacity. Helps determine whether the plant is water-stressed, has damaged roots, or is under environmental stress. Scenarios: smart planters, indoor green plant care, plant factories, research greenhouses. | 通过智能花盆或固定摄像头采集植物叶片的红外热成像图像（或普通RGB图像结合环境温湿度数据），利用AI模型估算叶片温度与空气温度的差值，结合辐射、湿度等参数（可由传感器提供或模型内估），计算植物蒸腾速率的相对值（0-100%）。蒸腾速率与根系吸水活力正相关，可间接反映根系健康及水分输送能力。该技能有助于判断植物是否缺水、根系受损或环境胁迫。应用场景：智能花盆、室内绿植养护、植物工厂、科研温室。
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 1479 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)
- +3Description length 919: 120–800 characters recommended
- +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
- +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: 66.