SKILLEMALL.ai

BF pet-vocal-emotion-analysis

Recognizes cat and dog barks through pet voiceprint AI, translates and outputs emotions and behavioral intentions such as happiness, excitement, anger, anxiety, pain, vigilance, and attention-seeking, enabling human-pet smart interaction. | 宠物叫声情绪解析技能,通过宠物声纹AI识别猫狗叫声,翻译输出开心、兴奋、愤怒、焦虑、痛苦、警惕、求关注等情绪与行为意图,实现人宠智能交互

ClawHub Agent Skills author: smyx-sunjinhui v1.0.13 MIT-0 30 files body ≈ 1 415 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 30/100 · Will not run — weak spots: steps, result and completion, when it triggers

AnalyzerInfrastructureWriting and documentstype 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
30/100
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 30/100

  • 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
  • 40Consistency. Frontmatter name (pet-vocal-emotion-analysis) differs from the folder (smyx-pet-vocal-emotion-analysis)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Execution cost. Instruction body is 1415 tokens
  • 100Running it twice. No mutating operations
  • low 10 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
  • -255 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 309: enough signal without eating the budget
  • +4Structure: 20 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 appears to provide cloud pet-sound analysis, but it also auto-creates persistent user identity, stores tokens locally, queries account-linked history, and ships with a default HTTP development configuration that contradicts its HTTPS privacy claim.
LLM: suspicious (high) · 9 Sept 2026