Analysis updated 2026-05-18
Audit and rewrite AI-generated marketing copy in Chinese or English to sound more natural
Review website or product interface designs for templated, AI-typical patterns
Build a reusable voice or design profile from real writing or design samples
Run a blind A/B comparison to test whether rewritten text is distinguishable from human writing
| voidful/michelin | 0marildo/imago | 0xdfi/glm-5.2-1m-4x-dgx-spark | |
|---|---|---|---|
| Stars | 3 | 3 | 3 |
| Language | Python | Python | Python |
| Setup difficulty | easy | easy | hard |
| Complexity | 2/5 | 2/5 | 5/5 |
| Audience | writer | general | researcher |
Figures from each repo's GitHub metadata at analysis time.
Copy the skill folder into an agent's skills directory, such as ~/.claude/skills/.
Michelin is a skill made for AI coding assistants that tries to make text and visual design written or reviewed by AI feel less like an AI wrote it, in both Traditional Chinese used in Taiwan and English. Rather than just swapping out a few overused words, it works in four steps: first identifying facts and constraints in the text that must not change, then figuring out what specifically feels templated, whether that is structure, tone, visual layout, or wording that does not match how people in Taiwan actually speak, then only fixing the specific issues found, and finally checking the result through a blind side-by-side comparison against real human writing. It can be used to review website copy, product interfaces, documents, presentations, and marketing materials, either checking existing drafts or helping build a sense of a person's or brand's voice and design style from real examples they provide. The project is explicit that outside AI-detection tools are not treated as a reliable judge of success, instead, success means the AI-written version becomes hard to tell apart from human writing in a matched blind comparison, without the quality getting worse just to pass the test. The repository includes a Python-based checker that scans text or web pages for common AI writing patterns and Taiwan-specific language signals, plus a set of scripts for running the anonymous comparison process: preparing test cases, collecting judgments from independent reviewers, and generating a report. It also includes a packaging script that bundles the skill into a distributable file with a checksum. The author notes clear limits: there is no single fixed definition of what sounds human, blind tests can only show something is hard to distinguish under that specific test setup rather than proving anything about its origin, and image or video content still needs human judgment. The project is unrelated to the Michelin Group or Michelin Guide and carries no official rating meaning. It is released under the MIT license.
A bilingual skill for AI assistants that identifies AI-sounding text or design and rewrites it, then verifies the result with a blind human comparison test.
Mainly Python. The stack also includes Python.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly writer.
This repo across BitVibe Labs
Verify against the repo before relying on details.