SKILLEMALL.ai

BF smyx-elderly-tv-sedentary-reminder-analysis

Using a fixed camera in the living room (aimed at the sofa and TV area), the system analyzes the elderly person's continuous sitting time while watching TV, detecting whether the body remains in a seated posture and the face is oriented toward the TV area (watching). | 通过客厅固定摄像头(对准沙发和电视区域),分析老年人连续观看电视的坐姿时长,检测人体是否持续处于坐姿且面部朝向电视区域(注视电视)。当连续坐姿观看电视超过预设阈值(默认2小时)且期间未起身活动时,输出'久坐活动提醒',建议老年人起身走动、做伸展运动。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.4 MIT-0 30 files body ≈ 1 710 tokens Open the sourceclawhub.ai analyzed 2 d ago

Using a fixed camera in the living room (aimed at the sofa and TV area), the system analyzes the elderly person's continuous sitting time while watching TV…

As a process F 32/100 · Will not run — weak spots: steps, result and completion, when it triggers

AnalyzerSoftware developmentInfrastructuretype 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
F
32/100
Will not run
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 32/100

  • 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
  • 21Steps. 1 steps, 1 vague phrases
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1710 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
  • -256 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 395: 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 skill is a cloud video-analysis tool, but it handles private home video and user/account tokens automatically with too little visible user control, so it should be reviewed before install.
LLM: suspicious (high) · 3 Sept 2026