What does this skill do, and when should you use it?
Humanizer is a portable agent skill for reducing recognizable signs of AI-generated writing. Based on Wikipedia’s “Signs of AI writing” guide, it covers 33 patterns across content, language, style, communication, filler, and hedging. It performs an initial rewrite, an audit for text that still looks obviously AI-generated, and a second rewrite. Users can also provide writing samples for voice calibration.
It reads the user’s text and, optionally, two or three paragraphs of the user’s own writing. It checks for 33 patterns, including significance inflation, promotional language, vague attributions, formulaic phrasing, AI-associated vocabulary, passive or subjectless fragments, dash overuse, chatbot closers, and excessive hedging. It produces a rewrite, audits the result for remaining AI signals, and applies a second rewrite.
- A content writer wants to reduce obvious AI-writing signals before publishing a draft.
- An editor needs to turn promotional, formulaic, or vague prose into more specific writing.
- A user wants an AI draft to better match their own sentence rhythm, word choices, and quirks.
- A user of Claude Code or another skill-compatible agent harness wants a plain-Markdown text-editing skill.
How do you install this skill?
- Write/Edit permissions may rewrite user text directly; confirm scope and retain the original or a recoverable copy.
- The rewriting objective may conflict with technical, legal, academic, or factual-accuracy requirements; review meaning, citations, and author voice manually.
- No Chinese-specific rules or execution evidence are provided, so Chinese-language effectiveness and false-edit risk are uncertain.
- The methodology relies on a Wikipedia guide, and the publisher is not verified by the curated enterprise registry.
- Shell / CLI
- Network access
- Local filesystem
Node.jsGit
With the Skills CLI, run npx skills add blader/humanizer. To update an existing installation, run npx skills update humanizer. To install across supported harnesses, run npx skills add blader/humanizer --agent '*'. Claude Code users can use the documented plugin commands, or copy SKILL.md into the harness’s expected skill directory. The supplied material does not specify the exact skill path.
git clone --depth 1 https://github.com/blader/humanizer.git ~/.claude/skills/humanizerGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Invoke it through the harness’s skill interface, for example /humanizer, then paste the text. You can also make a direct request such as Please humanize this text: [your text]. For voice calibration, provide two or three paragraphs of your own writing before supplying the text to rewrite. The exact trigger depends on the harness.
What are this skill's strengths and limitations?
- Documents 33 AI-writing patterns with before-and-after examples.
- Supports voice calibration from user-provided writing samples.
- Includes a final audit and second-pass rewrite.
- Uses plain Markdown and is described as portable across skill-compatible harnesses.
- Released under the MIT license.
- SKILL.md was not cached in the supplied material, so its actual frontmatter, full instructions, and implementation details cannot be verified.
- The material does not document a test suite, quality metrics, or platform-specific validation.
- It focuses on rewriting text rather than fact-checking, adding sources, or performing specialist editorial review.
- Aggressively removing AI signals may change the original voice or wording, so users should review the output.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Humanizer Writing Skill this page | 53 · Use with care | ★ 57k | 27d ago | MIT |
| No AI Slop | 59 · Recommended | ★ 12k | 1mo ago | MIT |
| Shuorenhua: Chinese AI-Tone Cleanup Skill | 65 · Recommended | ★ 2.1k | 25d ago | MIT |
| SOUL.MD — Agent Personality Builder | 49 · Use with care | ★ 697 | 9d ago | MIT |
| Humanizer — AI Writing Humanizer & Detector Skill | 67 · Recommended | ★ 282 | 1mo ago | MIT |
How did FollowSkills review this skill?
The skill declares only Read, Write, Edit, Grep, Glob, and AskUserQuestion, with no network, credential, or external-effect capability shown. However, Write/Edit access is broad and there is no explicit confirmation flow, sensitive-data handling, data-flow disclosure, or rollback procedure. The MIT license is clear, but publisher identity is unverified, so points are deducted.
The instructions contain extensive rules, examples, and semantic-preservation requirements, making the happy path plausible. There are no tests, CI evidence, abnormal-input procedures, or clear failure messages, and the referenced Process and Output section is not fully present in the supplied evidence. The score is therefore limited by static calibration and further reduced.
The audience, core input task, and optional voice-sample workflow are reasonably clear, and compatibility is declared as any-agent. Boundaries for false triggering and non-fit use cases, output formatting, and Chinese-language behavior are not sufficiently specified, so points are deducted.
The materials provide a name, version, installation instructions, usage examples, 33 pattern categories, version history, source attribution, and an MIT license. Documentation is readable, but runtime assumptions, troubleshooting, maintenance ownership, and update responsibility are incomplete, with minor detail inconsistencies between README.md and SKILL.md.
The broad rule set and many before/after examples plausibly support the core task of removing common AI-writing signals. There is no committed output evaluation, test evidence, or comparative evidence, and rigid rewriting, paragraph-preservation, and personality guidance may alter meaning or author voice. The output format is also unspecified, so points are deducted.
The README identifies Wikipedia and WikiProject AI Cleanup as methodological sources and includes a version history, providing limited auditability. There is no CI, test suite, reproducible experiment, or third-party execution evidence; conclusions therefore rely mainly on static claims and are constrained by the static-review cap.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
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