humanizer
Strip AI writing patterns from text. Checks 24 patterns across 4 categories (structural, lexical, rhetorical, formatting) with academic economics adaptation. This skill should be used on any text that reads too "AI-generated", or as a final pass on drafted sections. Triggers on "humanize", "de-AI", "make it sound natural", or "strip AI patterns".
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill humanizer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Humanizer Strip AI writing patterns from academic text while preserving economics content and formal structure. **Input:** `$ARGUMENTS` — path to file to humanize. --- ## Workflow ### Step 1: Read the File Read the target file from `$ARGUMENTS`. Support `.tex`, `.md`, `.txt`, and `.qmd` files. ### Step 2: Scan for AI Patterns Check all 24 patterns across 4 categories: #### Category 1: Structural
What does the humanizer skill do?
Strip AI writing patterns from text. Checks 24 patterns across 4 categories (structural, lexical, rhetorical, formatting) with academic economics adaptation. This skill should be used on any text that reads too "AI-generated", or as a final pass on drafted sections. Triggers on "humanize", "de-AI", "make it sound natural", or "strip AI patterns".
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill humanizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.