SKILLEMALL.ai

BF smyx-pet-treadmill-intensity-analysis

AI-powered pet treadmill exercise intensity analysis combined with optional heart-rate band data. Detects stride frequency, limb extension, and respiratory rate from treadmill video to assess current exercise load (Low/Medium/High) and provide real-time pacing suggestions. Scenarios: smart pet treadmills (dog/cat), pet weight-loss training centers, pet rehabilitation. | 通过宠物跑步机内置或外置摄像头实时分析宠物跑步视频,检测步频、四肢伸展幅度、呼吸频率等运动姿态指标,并结合可选的心率带数据(蓝牙心率监测),综合评估当前运动强度等级(低/中/高),辅助宠物主人科学控制运动量,防止过度疲劳或运动损伤。应用场景:宠物跑步机(犬用/猫用)、宠物减肥训练中心、宠物康复理疗。

ClawHub Agent Skills author: smyx-skills v1.0.0 MIT-0 31 files body ≈ 1 500 tokens Open the sourceclawhub.ai analyzed 2 d ago

AI-powered pet treadmill exercise intensity analysis combined with optional heart-rate band data.

As a process F 35/100 · Will not run — References files that are not bundled: reportImageUrl

AnalyzerMedia and videoSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
65
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: reportImageUrl
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: smyx-pet-treadmill-intensity-analysis (ClawHub)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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: 0. 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")
  • warning missing-ref reference to a missing file: reportImageUrl

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: reportImageUrl
  • 0Tools and files. 1 referenced file(s) missing: reportImageUrl
  • 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
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1500 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)
  • +3Output format is not stated: the model decides each time
  • -215 emoji in the instructions: noise for the model
  • -33 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 523: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: suspicious
This skill appears intended for pet treadmill video analysis, but it also uploads media, links results to personal identifiers, retrieves report links, creates or reuses backend accounts, and stores tokens locally with incomplete user-facing controls.
LLM: suspicious (high) · 9 Jun 2026