SKILLEMALL.ai

AD smyx-crop-pest-identification-analysis

Triggers when a user provides images or videos of crop leaves, buds or fruits (local file or URL) for pest identification; calls server-side APIs to detect common agricultural pests such as aphids, red spider mites, cotton bollworms and corn borers, outputting pest types with confidence scores. | 当用户提供作物叶片、嫩芽或果实的图像/视频(本地文件或网络URL)时,触发本技能进行虫害识别;调用服务端API检测蚜虫、红蜘蛛、棉铃虫、玉米螟等常见农业害虫,输出虫害类型与置信度。应用场景:番茄/玉米/花生的虫害早期发现与精准施药,减少农药滥用。

ClawHub Agent Skills author: smyx-skills v1.0.9 MIT-0 30 files body ≈ 1 302 tokens Open the sourceclawhub.ai analyzed 2 d ago

Triggers when a user provides images or videos of crop leaves, buds or fruits (local file or URL) for pest identification; calls server-side APIs to detect…

As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions

AnalyzerMedia and videoSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
41/100
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

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 41/100

    • 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
    • 25Steps. 1 steps
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1302 tokens
    • 100Running it twice. No mutating operations
    • low 11 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 421: enough signal without eating the budget
    • +4Structure: 21 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: 81.

    External checks

    ClawHub: suspicious
    The skill performs crop pest analysis, but it also silently manages identities, stores tokens locally, and retrieves cloud history, so it needs user review before installation.
    LLM: suspicious (high) · 24 Aug 2026