SKILLEMALL.ai

AD smyx-seed-germination-rate-prediction-analysis

Triggers when a user provides a seedling tray image or video for analysis; uses AI object detection to identify emerged seedlings (cotyledons breaking through soil or fully expanded), counts germinated seeds, and compares with total sown seeds to estimate germination rate. Application scenarios: smart seedling incubators, greenhouse nursery trays, home planting pots, seed company germination tests. | 通过育苗盘上方的固定摄像头,播种后连续采集土壤表面图像,利用AI目标检测模型识别出土幼苗,计数已发芽的种子数量,并与播种总数对比估算发芽率。应用场景:智能育苗箱、温室育苗盘、家庭播种盆、种子公司发芽试验。

ClawHub Agent Skills author: smyx-skills v1.0.14 MIT-0 30 files body ≈ 1 324 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerMedia and videoInfrastructuretype 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 1324 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
    • -255 emoji in the instructions: noise for the model
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 506: 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: 81.

    External checks

    ClawHub: suspicious
    The skill’s seed-analysis purpose is understandable, but it adds under-disclosed identity handling, persistent token storage, and default plaintext HTTP service calls.
    LLM: suspicious (high) · 8 Sept 2026