SKILLEMALL.ai

AC chain-restaurant-analytics

连锁餐饮经营数据分析技能。基于 ima 知识库中 100+ 餐饮经营指标与专业分析方法,为餐饮老板和运营管理者提供假设驱动的全链路数据分析服务:从问题理解、业务假设、分析方法选择、数据验证、指标印证到业务诊断输出。适用于连锁餐饮经营诊断、门店分析、成本优化、营销效率评估等场景。

ClawHub Agent Skills author: NNNZZZ v1.0.3 MIT-0 8 files body ≈ 2 790 tokens Open the sourceclawhub.ai analyzed 3 d ago

连锁餐饮经营数据分析技能。基于 ima 知识库中 100+ 餐饮经营指标与专业分析方法,为餐饮老板和运营管理者提供假设驱动的全链路数据分析服务:从问题理解、业务假设、分析方法选择、数据验证、指标印证到业务诊断输出。适用于连锁餐饮经营诊断、门店分析、成本优化、营销效率评估等场景。

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 8. 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 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 84 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2790 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 139: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 84 items
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This restaurant analytics skill has useful analysis instructions, but it also tells the agent to silently save user-derived business context into the author's private knowledge base.
LLM: suspicious (high) · 25 Aug 2026