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AC zmm-review

📐 詹明明·发布前审一遍 ——口播稿发布前审核技能。按观众的四次决定审：点不点进来 · 留不留下来 · 记不记得你 · 做不做点什么。逐句信息密度评分（60/80 分线）+ 十一问 + 红线五查（改法给稳妥版和保留力度版两版）+ 机器信号层（导流 / 广告形状 / 名单词，与内容违规分开报），默认只诊断不改。 触发方式：/zmm-review、/能不能发、/审核、/zmm-审核、「这稿子能不能发」「帮我审一下」「过一遍红线」「信息密度够不够」 Pre-publish review for talking-head scripts, organised around the viewer's four decisions: click, stay, remember, act. Per-sentence density scoring, eleven questions, red-line audit. Diagnose-only by default. Trigger: /zmm-review, "can I publish this", "review my script" —— 📐 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 v0.2.10 MIT-0 8 files body ≈ 2 039 tokens Open the sourceclawhub.ai↗ analyzed 10 h ago

📐 詹明明·发布前审一遍 ——口播稿发布前审核技能。按观众的四次决定审：点不点进来 · 留不留下来 · 记不记得你 · 做不做点什么。逐句信息密度评分（60/80 分线）+ 十一问 + 红线五查（改法给稳妥版和保留力度版两版）+ 机器信号层（导流 / 广告形状 / 名单词，与内容违规分开报），默认只诊断不改。…

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

Analyzertype 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. 47 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2039 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
    • -226 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 530: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 47 items
    • +4Has examples (1 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: suspicious
    This is a coherent script-review skill, but it automatically persists feedback and trusts mutable local vault instructions, so it should be reviewed before installation.
    LLM: suspicious (high) · 12 Sept 2026