BD smyx-grooming-effect-assessment-analysis
AI-powered pet grooming effect assessment: detects mat residue area, dandruff coverage, and coat smoothness from post-grooming images, outputs a 0-100 grooming score with targeted re-grooming suggestions. Scenarios: daily home grooming, pet salon quality check, long-hair cat/dog shedding season management. | 通过智能梳毛器或普通摄像头拍摄梳毛后的宠物皮肤和毛发高清图像，利用AI图像识别技术检测毛结团块残留面积、皮屑覆盖率以及毛发顺滑度，自动评估本次梳毛效果，并提示是否需要进行二次梳理或进一步护理。有助于宠物主人判断梳毛是否彻底，预防毛球症和皮肤问题。应用场景：宠物家庭日常梳理、宠物美容店服务质检、长毛猫/犬换毛期管理。
As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 35/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1343 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
- -259 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 468: 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.