BF plant-nutrition-diagnosis-analysis
Diagnoses plant nutrient deficiency or excess based on computer vision and plant physiology, outputs targeted fertilization suggestions for precision nutrient management. | 植物营养诊断技能,基于计算机视觉与植物生理学,通过叶片特征诊断氮磷钾及微量元素缺乏或过剩,输出精准施肥建议
As a process F 19/100 · Will not run — References files that are not bundled: references/api_doc.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
For the model run — optional
- 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: 29. 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") - warning
missing-refreference to a missing file: references/api_doc.md
Process rating: all ten parameters 19/100
Will not run. References files that are not bundled: references/api_doc.md
- 0Tools and files. 1 referenced file(s) missing: references/api_doc.md
- 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
- 40Consistency. Frontmatter name (plant-nutrition-diagnosis-analysis) differs from the folder (smyx-plant-nutrition-diagnosis-analysis)
- 100Execution cost. Instruction body is 1427 tokens
- 100Running it twice. No mutating operations
- low 10 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
- -255 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 226: enough signal without eating the budget
- +4Structure: 20 headings
- +4Has examples (3 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.
External checks
ClawHub: suspicious
This skill can analyze plant images, but it also silently creates or reuses cloud identities, uploads user media, queries cloud history, and stores tokens locally with incomplete disclosure.
LLM: suspicious (high) · 27 Aug 2026