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How to strip AI tells from a blog post before publishing with Humanizer Stack

Run the word-level pass and scanner on an AI-drafted post so it reads like a person wrote it.

  • Top pick
  • Content
  • Intermediate
  • 30 minutes
  • From the README
  • Claude Code

Who it is forA founder or content marketer who has an AI-drafted post that needs to sound like their own voice before it goes live.

The problemThe draft carries word-level tells such as em dash overuse, hype vocabulary, and negative parallelism that readers notice.

The outcomeA revised draft with the flagged tells fixed, plus a short list of scanner hits the author reviewed and kept on purpose.

What you need first

  • Claude Code installed, since the passes are packaged as Claude Code Skills
  • git and a terminal to clone the repo and run install.sh
  • Python 3 to run the scanners
  • A draft file in plain text or Markdown to work on

Steps

  1. git clone https://github.com/NulightJens/humanizer-stack.git
  2. cd humanizer-stack
  3. ./install.sh
  4. python3 scripts/copy_scan.py draft.md
  5. In Claude Code, send: humanize this post, then rerun python3 scripts/copy_scan.py draft.md to confirm the hits are gone or marked as intentional.

Prompt for your agent

Using the humanizer-stack repo at <repo path>, run python3 scripts/copy_scan.py on <draft file> and then apply the humanizer skill to fix every flagged word-level tell. Output the revised file and any remaining scanner hits. Verify that the rerun of the scanner shows no hits except lines the author marked copy-ignore.

Caveat. The scanners catch only the pattern-matchable slice, so a human reader still has to judge cadence and polished but empty filler.

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