SKILLEMALL.ai

BF smyx-elderly-loneliness-depression-analysis

Using fixed cameras at home (living room, bedroom) of elderly people living alone, the system analyzes daily videos and detects negative behavior indicators during solo time: dazing (long-duration motionless gazing without purposeful action), sighing (rapid chest rise-and-fall with audible expiration), and self-talking (mouth movement without any conversation partner). | 通过独居老人在家中的固定摄像头(如客厅、卧室),分析日常视频,检测独处期间的消极行为指标:发呆(长时间静止注视,缺乏目的性动作)、叹气(胸部快速起伏伴呼气声)、自言自语(口部活动但无对话对象)等。该技能可辅助家属或社区工作者了解老人心理状态,及时进行情感关怀或心理干预。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.8 MIT-0 30 files body ≈ 1 700 tokens Open the sourceclawhub.ai analyzed 32 h ago

Using fixed cameras at home (living room, bedroom) of elderly people living alone, the system analyzes daily videos and detects negative behavior indicators…

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

AnalyzerMedia and videoSoftware developmenttype 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
  • 25Steps. 1 steps
  • 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 1700 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 509: 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’s core video-analysis purpose is coherent, but it handles very sensitive home and mental-health data while automatically creating/reusing identities, contacting cloud services, and persisting tokens without enough user control.
LLM: suspicious (high) · 25 Aug 2026