SKILLEMALL.ai

BD smyx-commuter-fatigue-care-analysis

Through a fixed camera in a smart-home living room, the system analyzes office worker behavior in the first 30 minutes after coming home, detecting slumped sitting / reclining (relaxed posture, back-to-sofa angle > 120°), facial fatigue features (visible eye bags, downturned mouth corners, frequent blinking), and sighing frequency (rapid chest/abdomen rise-fall with audible exhale). | 通过智能家居客厅的固定摄像头,分析上班族回家后30分钟内的行为,检测瘫坐/斜躺(姿态放松、背部与沙发夹角>120°)、面部疲惫特征(眼袋明显、嘴角下垂、频繁眨眼)、叹气频次(胸腹快速起伏伴呼气声)。当疲劳指数超过阈值时,通过智能音箱主动播报关怀语音(如'辛苦了,喝杯水休息一下'),并播放舒缓音乐。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.9 MIT-0 30 files body ≈ 1 978 tokens Open the sourceclawhub.ai analyzed 2 d ago

Through a fixed camera in a smart-home living room, the system analyzes office worker behavior in the first 30 minutes after coming home, detecting slumped…

As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerSoftware 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
D
35/100
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 35/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1978 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
  • -264 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 537: 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 has a plausible fatigue-care purpose, but it handles private home camera footage with silent identity/account storage and unsafe unencrypted service settings, so it should be reviewed before installation.
LLM: suspicious (high) · 9 Sept 2026