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AC humanizer

Detects 43 AI writing patterns and rewrites text in 5 voice profiles. Use when (1) AI text reads like a chatbot, (2) preparing content for publication, (3) auditing prose for AI tells, (4) editing a file in place. Outputs a 0-100 AI-tell score on demand. Pure Markdown, zero dependencies, no network calls.

ClawHub Agent Skills author: Adam Boudj v0.4.0 MIT-0 2 files body ≈ 11 918 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructureWriting and documentsCustomer supporttype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 11918 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (humanizer) differs from the folder (humanizer-skill)
  • 40Execution cost. Instruction body is 11918 tokens: crowds the task out of the window
  • 70Failures and branches. 28 branches
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 94 steps
  • 100Running it twice. Mutating operations check current state
  • high The skill tells the model to perform an irreversible action with no human approval

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 306: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 94 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This is an instruction-only writing tool with no code or network access, but it explicitly optimizes text to avoid AI-detection signals and can edit local files in place.
LLM: suspicious (high) · VirusTotal: · 29 May 2026