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AC zmm-concept

📐 詹明明·重讲一个概念 ——重讲一个大家都在用的概念（定价 / 选题 / 需求 / 转化 / 获客…）——拆开它，从裂缝里长出一把尺子。先锁四样再写稿，最后由本人口述定稿。 触发方式：/zmm-concept、/重讲概念、「把 XX 这个概念讲清楚」「XX 到底是什么」「为什么 XX 越 YY 越 ZZ」「掰开揉碎讲一个概念」 Re-explain a concept the audience already uses: crack it open, hand them a ruler. Lock four things before drafting; final version comes from the host's own spoken take. Trigger: /zmm-concept, "explain X properly", "why does more X lead to less Y" —— 📐 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 v0.2.9 MIT-0 4 files body ≈ 2 005 tokens Open the sourceclawhub.ai↗ analyzed 34 min ago

📐 詹明明·重讲一个概念 ——重讲一个大家都在用的概念（定价 / 选题 / 需求 / 转化 / 获客…）——拆开它，从裂缝里长出一把尺子。先锁四样再写稿，最后由本人口述定稿。 触发方式：/zmm-concept、/重讲概念、「把 XX 这个概念讲清楚」「XX 到底是什么」「为什么 XX 越 YY 越…

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

GeneratorMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
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: 4. 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. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2005 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
    • -243 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 447: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a disclosed Chinese content-workflow skill for drafting concept videos; it reads and updates a content vault but shows no hidden code, network access, credential use, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 12 Sept 2026