Claude Code Skill: Humanize AI-Written Text by Fixing Structural Patterns (Measured Humanizer)

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.95 · Level: intermediate
Categories: Quality Control, Context & Memory, Debugging, CLAUDE.md, Skills
Original source: r/ClaudeAI post/comment

What problem this solves

Making AI-generated text sound more human by identifying and correcting structural patterns (like paragraph length variance and first-person usage) that differentiate human from AI writing, rather than relying on ineffective word-level fixes.

Summary

This workflow provides a Claude Code skill that audits and iteratively fixes text drafts to make them sound more human. It's based on research comparing human and AI-written blog posts, identifying structural differences like paragraph length variance and first-person pronoun usage as key 'AI tells'. The skill scores a draft, identifies the most off-target structural count, fixes it, and repeats for up to four passes.

Why it is useful

This workflow is valuable because it provides a concrete, research-backed, and open-source Claude Code skill to address a common and challenging problem: making AI-generated text sound more human. It moves beyond superficial word-level fixes to target deeper structural patterns, offering a practical, repeatable, and validated method for improving writing quality. The clear instructions for installation and use make it highly accessible to other Claude Code users.

Workflow

1. Conduct research by analyzing human and AI-generated texts to identify structural patterns that differentiate them (e.g., paragraph length variance, first-person usage).
2. Develop a Claude Code skill that incorporates these identified patterns and their thresholds.
3. Calibrate the skill's thresholds by testing against known human and AI texts to ensure it passes human texts and flags AI texts appropriately.
4. To use the skill directly: Clone the measured-humanizer repository from GitHub.
5. Run the style_gate.js script with your draft file (e.g., node measured-humanizer/skills/measured-humanizer/gate/style_gate.js YOUR_DRAFT.md --brief).
6. Alternatively, install as a Claude Code plugin: Use /plugin marketplace add SadhvikChirunomula/measured-humanizer followed by /plugin install measured-humanizer@measured-humanizer.
7. Ask Claude Code to 'humanize' or 'audit' a draft using the installed skill.
8. Review the skill's output, which identifies the most off-target structural count and applies a fix.
9. Repeat the audit/fix process up to four times as the skill iteratively refines the text.

Tools / artifacts

• Claude Code
• Git
• Node.js (version 14+)
• measured-humanizer (GitHub repository/skill)
• Markdown files (for drafts)

Validation signals

• Research-backed methodology: Counted 16 things in ~100 blog posts (36 human, 45 AI) to identify actual differentiators.
• Empirical findings: Discovered 'shape' (paragraph length variance, first-person usage) as key, while common 'AI tells' (hedging words, 'robust') were ineffective.
• Performance metrics: 75% of human posts pass the skill, and only 11% of generated posts slip through.
• Iterative refinement: The skill performs up to four passes to fix issues, indicating a structured approach to improvement.
• Open-source and MIT licensed, allowing for community review and validation.

Limitations

• Minimal community validation due to the post's recency.
• Requires Node.js and basic comfort with CLI commands for setup and direct execution.
• The research focused on blog posts, so its applicability to other text types might vary, though the principles are likely broadly useful.

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