SKILLEMALL.ai

AC zmm-revenue

📐 詹明明·这个月钱去哪了 ——营收异动归因。这个月钱少了(或多了),到底是客人变少、每人买得少、还是单价变了——三种原因的处理动作完全相反,分不清就会做反。也识别「静默侵蚀」:每个月只跌一点点、单月都像正常波动,累计起来致命。 触发方式:/zmm-revenue、/钱去哪了、/营收归因、「这个月为什么少了」「营收掉了」「钱去哪了」「生意怎么突然不行了」「涨了但我不知道为什么」 Revenue movement attribution for owner-operators. Splits any change into customer count × per-customer volume × price, so the right fix follows. Also detects slow erosion that hides inside monthly noise. Works from a ledger or order book — no dashboard required. Trigger: /zmm-revenue, "why is revenue down this month", "where did the money go", "business suddenly slowed" —— 📐 詹明明 · 不给公式,给判据。每条规则都标了实测代价。

ClawHub Agent Skills author: 詹明明 v0.2.6 MIT-0 3 files body ≈ 1 225 tokens Open the sourceclawhub.ai analyzed 3 d ago

📐 詹明明·这个月钱去哪了 ——营收异动归因。这个月钱少了(或多了),到底是客人变少、每人买得少、还是单价变了——三种原因的处理动作完全相反,分不清就会做反。也识别「静默侵蚀」:每个月只跌一点点、单月都像正常波动,累计起来致命。…

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

AnalyzerInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
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

    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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"

    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. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1225 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

    • +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
    • +5Description quotes 3 example trigger phrases
    • +3Description length 598: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    This skill is focused on revenue attribution, but it reads shared memory and saves sensitive business conclusions automatically, so it should be reviewed before installation.
    LLM: suspicious (high) · 6 Sept 2026