SKILLEMALL.ai

AC restaurant-evaluator

餐饮创业选址与开店评估专家系统,基于"勇哥餐饮创业"视频内容整理的自测表。 用于评估餐饮选址质量、计算回本周期、判断创业可行性。 使用场景: 1. 餐饮选址评估 - 输入选址各项指标,自动计算得分并给出A/B/C/D评级 2. 开店可行性评估 - 评估资金、产品、运营能力等,给出能否开店的建议 3. 回本周期计算 - 输入投资金额和预估营收,计算回本周期 4. 避坑指南 - 识别快招加盟、网红店、大厨依赖等5种必死模型 5. 选择店模型 - 推荐夫妻店、蜜雪型、爆品店、早餐店等4种常胜模型 触发词:餐饮评估、选址评估、开店评估、勇哥自测、回本计算、餐饮创业

ClawHub Agent Skills author: wwek v1.0.1 4 files body ≈ 735 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 4. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 52 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 735 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 281: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 52 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This skill is a disclosed restaurant-location and startup calculator with a simple local Python script and no evidence of hidden data access or unsafe behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026