SKILLEMALL.ai

BF smyx-livestock-fever-detection-analysis

Detects abnormal body temperature rise or drop in livestock and poultry from thermal or visible-light imagery, and outputs fever/hypothermia early warnings based on visual thermal features. | 通过热成像或视觉特征识别畜禽体温异常,预警发热。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.10 MIT-0 30 files body ≈ 1 540 tokens Open the sourceclawhub.ai analyzed 2 d ago

Detects abnormal body temperature rise or drop in livestock and poultry from thermal or visible-light imagery, and outputs fever/hypothermia early warnings…

As a process F 31/100 · Will not run — weak spots: steps, result and completion, when it triggers

AnalyzerData and analyticstype 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
31/100
Will not run
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
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 31/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1540 tokens
  • 100Running it twice. No mutating operations
  • low 13 top-level sections: this looks like several domains in one skill

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
  • -267 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 216: enough signal without eating the budget
  • +4Structure: 31 headings
  • +4Has examples (8 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 appears intended for livestock temperature analysis, but it silently handles cloud identity, stores tokens locally, uploads media, and ships with development HTTP endpoints, so it needs careful review before installation.
LLM: suspicious (high) · 27 Aug 2026