BD smyx-flowering-date-prediction-analysis
AI-powered flowering-date prediction for ornamental/cut-flower plants. From fixed greenhouse cameras or drones, captures images of flower-bud developmental stages, combines environmental sensor data — cumulative temperature (Growing Degree Days, GDD) and accumulated light (PAR or daylight hours) — and uses a pre-trained phenology model to predict the full-bloom date within the next 3-7 days. Helps growers precisely schedule pollination, harvesting and tourism activities. Scenarios: smart-agriculture greenhouses, cut-flower production bases, botanical gardens, flower tourism parks. | 通过智慧农业温室中的固定摄像头或无人机拍摄植物花蕾发育阶段的图像，并结合环境传感器提供的温度累积（生长度日，GDD）、光照累积（光合有效辐射或日照时长）等数据，利用预训练的物候模型预测未来3-7天内的开花日期（花朵完全开放）。该技能有助于温室种植者精准安排授粉、采收或观光活动。应用场景：智慧农业温室、切花生产基地、植物园、花卉观光园区。
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 1395 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 759: 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.