SKILLEMALL.ai

BD smyx-rose-pest-disease-detection-analysis

AI-powered pest & disease detection for roses (Rosa spp.). From garden cameras or mobile phone images of leaves, young shoots and flower buds, detects common rose enemies including black spot (black round/irregular spots with yellow halo), powdery mildew (white powdery layer on leaves/shoots), spider mites (tiny red/white dots on leaf back with webbing in severe cases) and aphids (green/black clustered tiny insects on shoots and buds). Outputs pest/disease type, severity grade and general control suggestions. Helps gardeners detect issues early and act in time. Scenarios: home gardens, rose specialty gardens, courtyard landscaping, cut-flower production bases. | 通过庭院摄像头或手机拍摄月季/玫瑰的叶片、嫩芽、花苞图像,利用AI视觉分析技术检测黑斑病(叶面黑色圆形或不规则斑点,周围黄晕)、白粉病(叶片、嫩芽表面白色粉状霉层)、红蜘蛛(叶片背面细小红色或白色点状螨虫,严重时结网)、蚜虫(嫩芽、花苞上绿色或黑色聚集的小虫)等常见病虫害,输出病虫害类型及严重程度,并提供防治建议。该技能有助于月季种植者早期发现问题,及时采取措施。应用场景:家庭花园、月季专类园、庭院绿化、切花生产基地。

ClawHub Agent Skills author: smyx-skills v1.0.6 MIT-0 30 files body ≈ 1 480 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
D
35/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description 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 1480 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 882: 120–800 characters recommended
  • +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
  • +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.

External checks

ClawHub: suspicious
The skill appears to provide rose pest analysis, but it also silently creates or reuses cloud identities, stores reusable tokens locally, uploads media to remote services, and ships unsafe default networking/configuration behavior.
LLM: suspicious (high) · 8 Sept 2026