SKILLEMALL.ai

BF smyx-family-conflict-intensity-detect-analysis

Using a fixed camera with microphone in the living room, the system analyzes audio and video in real time, detecting sound intensity (dB) and the intensity of body movements (e.g., rapid hand waving, finger pointing, pushing, throwing objects). It comprehensively evaluates the family conflict intensity level (low / medium / high). | 通过客厅固定摄像头(含麦克风),实时分析音频和视频,检测声音强度(分贝)和肢体动作激烈程度(如快速挥手、戳指、推搡、摔物等)。综合评估家庭争吵的冲突强度等级(低/中/高),当强度达到中或高时,通过手机APP推送提醒(如'检测到高强度冲突,建议冷静沟通或暂时分开')。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.8 MIT-0 30 files body ≈ 1 740 tokens Open the sourceclawhub.ai analyzed 2 d ago

Using a fixed camera with microphone in the living room, the system analyzes audio and video in real time, detecting sound intensity (dB) and the intensity of…

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. 2 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 1740 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
  • -258 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 468: 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 handles very sensitive household audio/video but uses cloud services, dev network configuration, and local identity/token storage in ways users should review carefully before installing.
LLM: suspicious (high) · 26 Aug 2026