SKILLEMALL.ai

BF smyx-kitchen-stove-left-on-detection-analysis

Using a fixed kitchen camera (must be able to capture the stove area), the system analyzes video in real time to detect whether there is human activity in the kitchen area, and at the same time identifies stove flames or heat sources (e.g., thermal/infrared features) to determine whether the gas stove is on. | 通过厨房固定摄像头(需能拍摄到灶台区域)实时分析视频,检测厨房区域内是否有人体活动,同时识别灶台火焰或热源(如红外特征)以判断燃气灶是否处于开启状态。当检测到厨房无人连续超过预设时间(默认10分钟)且灶火仍处于开启状态时,输出'忘关火'预警,可联动智能燃气阀自动关闭阀门,并推送提醒至家属或护理人员手机,预防火灾和燃气泄漏事故。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.9 MIT-0 30 files body ≈ 1 601 tokens Open the sourceclawhub.ai analyzed 4 d ago

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

AnalyzerInfrastructureMedia and videotype 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 1601 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
  • -254 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 476: 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 does kitchen video analysis as advertised, but it also silently creates or reuses persistent user identity and token state while sending sensitive home video and identifiers to remote services.
LLM: suspicious (high) · 30 Aug 2026