SKILLEMALL.ai

BF smyx-reptile-thermoregulation-behavior-analysis

Through fixed enclosure cameras, the system analyzes behavior videos of reptiles (lizards, snakes, turtles) and detects movement frequency and dwell duration between the basking zone (heated area under the basking lamp) and the hiding zone (cave/cool side). | 通过爬宠箱固定摄像头,分析爬行动物(如蜥蜴、蛇、龟)的行为视频,检测宠物在晒点(加热灯下方高温区域)与躲避区(洞穴、冷区)之间的移动频次、停留时长以及活动节律。系统连续监测,生成每日温区利用报告,异常时推送提醒。

ClawHub Agent Skills author: smyx-skills v1.0.9 MIT-0 30 files body ≈ 2 438 tokens Open the sourceclawhub.ai analyzed 35 h ago

Through fixed enclosure cameras, the system analyzes behavior videos of reptiles (lizards, snakes, turtles) and detects movement frequency and dwell duration…

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. 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 2438 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
  • -271 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 366: 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 is a real cloud-backed reptile video analysis tool, but it silently manages user identity, uploads media, queries report history, and stores tokens locally with limited user control.
LLM: suspicious (high) · 24 Aug 2026