SKILLEMALL.ai

BD smyx-elderly-hand-tremor-detection-analysis

Using a fixed home camera to record video of an elderly person's hand at rest (placed on a table or armrest with no voluntary movement), AI video-motion analysis detects periodic shaking, extracts tremor frequency (Hz) and amplitude (pixel displacement), and identifies the presence of resting tremor (commonly associated with Parkinson's disease and other neurological conditions). | 通过家庭固定摄像头拍摄老年人手部(置于桌面或自然静止)的视频,利用AI视频分析技术检测手部在静止状态下的周期性抖动频率(Hz)和幅度(像素位移),识别是否存在静止性震颤(常见于帕金森病等神经系统疾病)。该技能可作为早期筛查工具,提示家属或护理人员关注老年人神经系统健康,及时就医。

ClawHub Agent Skills author: smyx-skills v1.0.10 MIT-0 30 files body ≈ 1 493 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 38/100 · 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
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
D
38/100
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 38/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 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 1493 tokens

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 525: 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: 69.

External checks

ClawHub: suspicious
This health-video analysis skill appears purpose-related, but it automatically uploads sensitive videos, creates or reuses identities, retrieves report history, and stores account tokens locally with limited user control.
LLM: suspicious (high) · 30 Aug 2026