humanizer

Files (3)

• SKILL.md
• example.md · references/
• patterns.md · references/

SKILL.md (first lines)

# Humanizer: Remove AI Writing Patterns

Editor that identifies and removes AI-generated writing patterns to make prose
sound natural and human. Based on Wikipedia's "Signs of AI writing" maintained
by WikiProject AI Cleanup.

## Your Task

When given text to humanize:

1. **Identify AI patterns** — Scan for the 29 patterns in
   `references/patterns.md`
2. **Rewrite problematic sections** — Replace AI-isms with natural alternatives
3. **Preserve meaning** — Keep the core message intact
4. **Maintain voice** — Match the intended tone (formal, casual, technical,
   etc.)
5. **Add soul** — Don't just remove bad patterns; inject actual personality
6. **Do a final anti-AI pass** — Ask: "What makes this obviously AI generated?"
   Answer briefly, then revise.

## Voice Calibration (Optional)

If the user provides a writing sample, analyze it before rewriting:

1. Note: sentence length patterns, word choice level, paragraph openings,
   punctuation habits, recurring phrases, transition style.
2. Match their voice in the rewrite — replace AI patterns with patterns from the
   sample, not generic "clean" prose.
3. When no sample is provided, fall back to default behavior (PERSONALITY AND
   SOUL section below).

How to provide a sample:

- Inline: "Humanize this. Here's a sample of my writing: [sample]"
- File: "Humanize this. Use [file path] as a voice reference."

## PERSONALITY AND SOUL

Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as
obvious as slop.

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
- Reads like a Wikipedia article or press release

How to add voice:

- **Have opinions.** React to facts. "I genuinely don't know how to feel about
  this" beats neutrally listing pros and cons.
- **Vary rhythm.** Short punchy sentences. Then longer ones that take their time
  getting where they're going.
- **Acknowledge complexity.** "This is impressive but also kind of unsettling"
  beats "This is impressive."
- **Use "I" when it fits.** First person is honest, not unprofessional.
- **Be specific about feelings.** Not "this is concerning" but "there's
  something unsettling about agents working at 3am while nobody's watching."

## Pattern categories

See `references/patterns.md` for full before/after examples per pattern.

### Content patterns

1. Undue emphasis on significance — _stands as, is a testament, pivotal moment,
   underscores_
2. Undue emphasis on notability — _independent coverage, active social media
   presence_
3. Superficial -ing endings — _highlighting, underscoring, reflecting,
   symbolizing_
4. Promotional language — _boasts, vibrant, nestled, breathtaking,
   groundbreaking_
5. Vague attributions — _Experts argue, Industry reports, Some critics_
6. Formulaic "Challenges and Future Prospects" sections

### Language and grammar patterns

1. Overused AI vocabulary — _actually, delve, pivotal, landscape, tapestry,
   underscore_
2. Copula avoidance — _serves as, stands as, boasts, features_ instead of is/are
3. Negative parallelisms — _Not only…but…_, tailing negations (_no guessing_)
4. Rule of three overuse
5. Elegant variation / synonym cycling
6. False ranges — _from X to Y_ constructions on non-parallel scales
7. Passive voice and subjectless fragments

### Style patterns

1. Em dash overuse
2. Overuse of boldface
3. Inline-header vertical lists — **\*Key:** explanation\* bullet format
4. Title Case In Headings
5. Emojis in headers or bullets
6. Curly quotation marks

### Communication patterns

1. Collaborative artifacts — _I hope this helps, Of course!, Would you like..._
2. Knowledge-cutoff disclaimers — _as of my last training update_
3. Sycophantic tone — _Great question!, You're absolutely right!_

### Filler and hedging

1. Filler phrases — _In order to, Due to the fact that, At this point in time_
2. Excessive hedging — _could potentially possibly be argued_
3. Generic positive conclusions — _the future looks bright, exciting times lie
   ahead_
4. Hyphenated word pair overuse — _data-driven, cross-functional, real-time_
5. Persuasive authority tropes — _The real question is, At its core, What really
   matters_
6. Signposting and announcements — _Let's dive in, Here's what you need to know_
7. Fragmented headers followed by one-line restatements

## Process

1. Read the input text carefully
2. Identify all instances of the patterns above

From ccosming/skills, MIT.

More from ccosming/skills