SKILLEMALL.ai

BF smyx-facial-hrv-trend-monitoring-analysis

Using everyday cameras (laptop camera, smartphone front camera, smart mirror), the system records 30-60 seconds of facial video and uses remote photoplethysmography (rPPG) to extract subtle color variations from facial skin micro-circulation, from which it computes heart-rate-variability (HRV) metrics including SDNN (standard deviation of all normal sinus RR intervals) and RMSSD (root mean square of successive RR-interval. | 通过日常摄像头(如电脑摄像头、手机前置摄像头)拍摄面部视频(30-60秒),利用光电容积描记技术(远程光电容积描记术,rPPG)提取面部皮肤微循环的微弱色度变化,从中计算心率变异性(HRV)指标,包括SDNN(全部正常窦性心搏间期的标准差)、RMSSD(相邻心搏间期差值的均方根)等。该技能可用于压力评估、心血管健康监测及疲劳管理。

ClawHub Agent Skills author: smyx-skills v1.0.10 MIT-0 30 files body ≈ 1 693 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

AnalyzerInfrastructureData and analyticsMedia 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. 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 1693 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 595: 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
The skill does HRV analysis through a remote backend, but it also silently creates or reuses persistent identities and stores account tokens for sensitive facial health data without clear user control.
LLM: suspicious (high) · 24 Aug 2026