BD smyx-plant-growth-stage-detection-analysis
AI-powered plant growth stage auto-detection from periodic full-plant images via smart pot / greenhouse fixed cameras. Recognizes key phenological features—cotyledon emergence, true-leaf count, flower bud differentiation, blooming, fruit setting, fruit ripening—and identifies the current developmental stage (germination, seedling, vegetative, flowering, fruiting, ripening), enabling precision irrigation/fertilization/lighting control and personalized growing guidance. Scenarios: smart pots, home grow boxes, greenhouses, plant factories. | 通过智能花盆或温室内固定摄像头，定期拍摄植物整体图像，利用AI视觉分析技术识别子叶展开、真叶数量、花芽分化、开花、结果、果实成熟等关键物候特征，自动判定植物当前所处的生长发育阶段（如发芽期、幼苗期、生长期、开花期、结果期、成熟期）。有助于精准农业管理，实现自动化灌溉、施肥、光照调节，并为用户提供种植指导。应用场景：智能花盆、家庭种植机、温室大棚、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 1662 tokens
- 100Running it twice. No mutating operations
- low 13 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
- -277 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 724: enough signal without eating the budget
- +4Structure: 23 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.