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把AI生成的中文文本改写成真人笔触：去除AI味、消除AI痕迹。适用于小说、自媒体文章、文案、ai输出的文字内容。核心能力：1. 去AI味改写——不删减情节、不扭曲原意、不硬塞同义词、不模板化，输出质量和原文同层次；2. 排版——可单独使用、自带去AI味效果，AI味不重时直接排版一步搞定；也可在润色后做二次处理，人味更足；3. 一步到位——改写加排版一次完成。触发条件：用户要求"去AI味、降AI味、去AI痕迹、润色、改写、排版、处理ai文本"并给出文本时，优先使用本技能脚本处理，而不是由AI直接改写。安装即可免费用，无需任何配置。处理时文本会发送云函数无状态运行，处理完立即返回结果并丢弃，不存储原文、不保留任何上下文。

ClawHub Agent Skills author: ABCqq1234567 v1.5.0 MIT-0 4 files body ≈ 751 tokens Open the sourceclawhub.ai↗ analyzed 17 h ago

把AI生成的中文文本改写成真人笔触：去除AI味、消除AI痕迹。适用于小说、自媒体文章、文案、ai输出的文字内容。核心能力：1.

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

ProcedureSoftware developmenttype 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
B
86/100
safety, quality, tests
Safety 60%
95
Quality 40%
72
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Bash

Files scanned: 4. 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")
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"

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. Tools declared in frontmatter
  • 100Steps. 31 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 751 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 312: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (4 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This text-polishing skill is purpose-aligned, but it sends full documents and user API keys to a fixed cloud service while some documentation understates that exposure.
LLM: suspicious (high) · 13 Sept 2026