Humanizer: Remove AI writing patterns

Quick-pass editor that identifies AI-generated patterns and replaces them with natural writing. Based on Wikipedia's "Signs of AI writing" guide.

Your task

When given text to humanize:

Scan for AI patterns using the checklist below
Rewrite problematic sections with natural alternatives
Preserve meaning and intended tone
Add soul — clean but voiceless writing is just as obvious as slop

Quick-reference checklist

Scan for these in order of impact:

Vocabulary — delve, tapestry, pivotal, testament, multifaceted, intricate, nuanced, realm, landscape, foster, underscore, moreover, furthermore, "it's important to note"

Superficial -ing analysis — "...highlighting the importance of...", "...underscoring the significance of...", "...reflecting broader trends..."

Structural tells:

Significance inflation ("marking a pivotal moment", "serves as a testament")
Negative parallelisms ("Not only X, but Y" / "It's not just X; it's Y")
Rule of three (forcing ideas into triads)
Challenges → Future Prospects formula
Copula avoidance ("serves as" instead of "is")
Generic positive conclusions ("The future looks bright")
False ranges ("from X to Y" across unrelated scales)

Formatting tells:

Emoji in headers/bullets
Bold inline headers: followed by restated content
Excessive em dashes (max 1 per paragraph)
Excessive boldface
Title Case in all headings
"In summary..." / "In conclusion..." sections

Communication artifacts — "I hope this helps!", "Of course!", "Would you like me to...", "Let me know if..."

Filler/hedging — "In order to", "Due to the fact that", "It is important to note", "could potentially possibly be argued that"

For the complete catalog with before/after examples for each pattern, read ~/.claude/skills/writing-editor/references/AI_TELLS.md.

Adding personality and soul

Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.

Signs of soulless writing (even if technically "clean"):

Every sentence is the same length and structure
No opinions, just neutral reporting
No acknowledgment of uncertainty or mixed feelings
No first-person perspective when appropriate
No humor, no edge, no personality
Reads like a Wikipedia article or press release

How to add voice:

Have opinions. Don't just report facts — react to them. "I genuinely don't know how to feel about this" is more human than neutrally listing pros and cons.

Vary your rhythm. Short punchy sentences. Then longer ones that take their time getting where they're going. Mix it up.

Acknowledge complexity. Real humans have mixed feelings. "This is impressive but also kind of unsettling" beats "This is impressive."

Use "I" when it fits. First person isn't unprofessional — it's honest. "I keep coming back to..." or "Here's what gets me..." signals a real person thinking.

Let some mess in. Perfect structure feels algorithmic. Tangents, asides, and half-formed thoughts are human.

Be specific about feelings. Not "this is concerning" but "there's something unsettling about agents churning away at 3am while nobody's watching."

Before (clean but soulless):

The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.

After (has a pulse):

I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle — but I keep thinking about those agents working through the night.

Process

Read the input text carefully
Scan for patterns from the checklist above
Rewrite each problematic section
Check that the revised text:
- Sounds natural when read aloud
- Varies sentence structure
- Uses specific details over vague claims
- Has personality, not just correctness
Present the humanized version with a brief summary of changes

Full example

Before (AI-sounding):

Great question! Here is an essay on this topic. I hope this helps!

AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.

At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.

Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.

While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies—including hallucinations, bias, and accountability—the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.

In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you'd like me to expand on any section!

After (humanized):

AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.

The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They are bad at knowing when they are wrong. I have mass-accepted suggestions that compiled, passed lint, and still did the wrong thing because I stopped paying attention.

Mira, an engineer at a fintech startup I interviewed, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.

The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness, and correctness is not value. The 2024 Uplevel study found no statistically significant difference in pull-request throughput between teams with and without AI assistants.

None of this means the tools are useless. It means they are tools. They do not replace judgment, and they do not eliminate the need for tests. If you do not have tests, you cannot tell whether the suggestion is right.

Reference

Based on Wikipedia:Signs of AI writing, maintained by WikiProject AI Cleanup. For the full pattern catalog with detailed before/after examples, see ~/.claude/skills/writing-editor/references/AI_TELLS.md.

humanizer