BD smyx-chinese-herbal-ingredient-trend-analysis
AI-powered active-ingredient accumulation trend assessment for medicinal herbs (e.g. honeysuckle, wolfberry, astragalus, danshen). Uses high-resolution leaf images captured by fixed cameras or drones in TCM cultivation bases, analyzes leaf color saturation, hue angle, relative chlorophyll content (estimated via color indices) and leaf thickness (inferred from edge focus / silhouette), and compares against the cultivar's standard reference atlas (typical features at peak active-ingredient stage) to output an accumulation trend level (Low / Medium / High / Peak). Helps determine the optimal harvest window and improve herb quality. Scenarios: TCM planting bases, GAP bases, herb cooperatives, raw-material bases for pharmaceutical companies. | 通过中药种植基地的固定摄像头或无人机拍摄药用植物（如金银花、枸杞、黄芪、丹参等）叶片的高清图像，利用AI视觉分析技术评估叶片颜色饱和度、色相角、叶绿素相对含量（通过颜色指数估算）以及叶片厚度（通过边缘聚焦或侧影估算），与品种标准图谱（特定生长阶段/有效成分积累峰值期的典型特征）进行对比，输出有效成分积累趋势等级（低/中/高/峰值）。该技能有助于确定最佳采收期，提高药材品质。应用场景：中药种植基地、GAP种植基地、中药材合作社、药企原料基地。
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 1475 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 971: 120–800 characters recommended
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -255 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.