SKILLEMALL.ai

AC zmm-product

📐 詹明明·我该卖什么 ——产品提炼技能。把碎片化的认知、经验、资源、已有产出,提炼成一个可交付、可售卖的东西。 广义产品:实物、线上产品、线下服务、课程、一套框架或理论、一个营收增长点,都算。 触发方式:/zmm-product、/提炼产品、/我该卖什么、/zmm-产品、「我有资源但不知道做什么产品」「我这个算产品吗」「怎么把我会的东西变成能卖的」「产品没特色」 Turn scattered experience, context and existing output into one deliverable worth paying for. Trigger: /zmm-product, "what should I sell", "is this actually a product", "how do I package what I know" —— 📐 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 ≈ 1 091 tokens Open the sourceclawhub.ai analyzed 3 d ago

📐 詹明明·我该卖什么 ——产品提炼技能。把碎片化的认知、经验、资源、已有产出,提炼成一个可交付、可售卖的东西。 广义产品:实物、线上产品、线下服务、课程、一套框架或理论、一个营收增长点,都算。…

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

ReferenceInfrastructuretype 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. 14 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1091 tokens
    • 100Running it twice. No mutating operations
    • low 12 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

    • +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 418: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 14 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 appears to be a legitimate product-advice workflow, but it relies on mutable external rule files and persistent memory in ways users should review before installing.
    LLM: suspicious (high) · 6 Sept 2026