BD smyx-fruit-ripeness-grading-analysis
AI-powered fruit ripeness grading for tomatoes / strawberries. From smart grow-boxes or mobile phone images, uses AI vision to detect fruit color (green / light green / orange / red / dark red), colored-area ratio, gloss, and relative fruit size (against a reference object), and outputs a ripeness grade (Mature-Green / Turning / Ripe / Over-Ripe) based on preset standards. Helps growers identify the optimal harvest window and ensures flavor and shelf quality. Scenarios: smart grow-boxes, greenhouses, home vegetable gardens, fruit & vegetable cooperatives. | 通过智能种植箱或手机拍摄的果实图像，利用AI视觉分析技术检测果实的颜色（绿/浅绿/橙/红/暗红）、着色面积比例、光泽度以及果实大小（相对于参照物），根据预设的成熟度分级标准输出等级（青熟期/转色期/成熟期/过熟期）。该技能帮助种植者确定最佳采收时机，保证果实口感和商品性。应用场景：智能种植箱、温室大棚、家庭菜园、果蔬合作社。
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 1386 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 727: 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.