BD smyx-flowering-fruit-set-rate-analysis-analysis
AI-powered flowering and fruit-set rate analysis for tomato / chili plants. From home grow-box or mobile phone images of flowering/fruit clusters, uses object-detection models to count open flowers (fully-opened corolla with visible stamens) and successfully-set young fruits (enlarged ovary, ~0.5-1cm green baby fruits), and computes fruit-set rate = young fruits / flowers × 100%. Helps growers evaluate pollination, nutrition and environmental adaptability, and guides hand-assisted pollination or water/fertilizer adjustment. Scenarios: home smart grow-boxes, greenhouses, balcony vegetable gardens. | 通过家庭种植箱或手机拍摄的植株花果图像（包含花穗、果实区域），利用AI视觉目标检测模型识别开放花朵（花冠完全展开、雄蕊可见）的数量以及已坐果的小果（子房膨大、直径约0.5-1cm的绿色幼果）数量，计算坐果率（小果数/花朵数 × 100%）。该技能帮助种植者评估授粉效果、营养状况及环境适应性，指导人工辅助授粉或调整水肥管理。应用场景：家庭智能种植箱、温室大棚、阳台菜园。
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 1442 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)
- +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
- +3Description length 791: 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: 69.