/humanize

name: humanize
description: 'Detects and removes AI-generated writing patterns while preserving meaning and facts. Triggers on: "humanize text", "make this sound human", "remove AI patterns", "rewrite to sound natural", "make this less AI", "de-slop this", "not sound like ChatGPT", "human pass".'
metadata:
  version: 1.0.1
  category: review
  tags: [writing, ai-detection, natural-language, rewriting]
  difficulty: intermediate

Humanize: AI Pattern Detection and Removal

Remove AI-generated writing patterns from text. Produce natural, human-sounding output that preserves meaning.

This is not a generic rewriter. It targets specific, documented AI-writing patterns catalogued by Wikipedia's WikiProject AI Cleanup from thousands of observed instances.

Workflow

Five phases. Each phase has a clear input, transformation, and output. Do not skip phases.

Phase 1: Detection Scan

Read the input text. Load `references/detection-patterns.md`. Scan for two categories of signals:

**A. Lexical patterns** (the 24 catalogued AI-writing patterns):

| Category | Patterns | Priority | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | | Content inflation | Significance puffing, notability claims, superficial -ing analyses, promotional language, vague attributions, formulaic challenges sections | HIGH — loudest AI tells | | Vocabulary | AI-frequency words, copula avoidance, filler phrases, excessive hedging | HIGH — statistically detectable | | Structure | Rule of three, negative parallelisms, elegant variation, false ranges, inline-header lists | MEDIUM — structural fingerprints | | Style | Em dash overuse, boldface overuse, title case headings, emoji decoration, curly quotes | MEDIUM — formatting tells | | Communication | Chatbot artifacts, knowledge-cutoff disclaimers, sycophantic tone, generic conclusions | LOW — obvious, usually caught by author |

**B. Statistical regularity signals** (see `references/statistical-signals.md`):

| Signal | What to look for | | ------------------------------- | ---------------------------------------------------------------------------------------------- | | Sentence length uniformity | Sentences clustering within a narrow word-count range | | Low clause density variation | Every sentence has the same number of clauses | | Flat information density | Every sentence carries roughly the same amount of detail | | High-frequency phrase templates | Stock collocations and common bigrams/trigrams dominating the text | | Excessive transition markers | Formal connectives appearing more than 8 per 1,000 words | | Structural symmetry | Paragraphs and sentences following balanced, mirror-like patterns | | Uniform inter-sentence cohesion | Every sentence tightly follows the previous with no topic shifts or digressions | | Generic function word usage | Connectors and prepositions used in textbook-standard distribution with no personal tendencies |

Output a detection report using the detection report template (see Output Format).

**Instance severity rating:**

| Severity | Criteria | | -------- | --------------------------------------------------------------------------------------------------------------- | | HIGH | 3+ patterns co-occurring in a single paragraph, or any paragraph saturated with AI vocabulary (5+ signal words) | | MEDIUM | 1-2 patterns in a paragraph, or a statistical signal present across 3+ consecutive sentences | | LOW | Isolated single instance of any pattern, or a borderline statistical signal |

Phase 2: Structural Rewrite

Transform document structure to break AI-typical organization:

• Convert uniform paragraph lengths to varied blocks
• Merge or split sentences to break rhythmic uniformity
• Reorder clauses where meaning permits
• Convert formulaic list structures to narrative where appropriate
• Remove tripartite constructions unless the content genuinely has three parts

Do not change factual content. Do not add information. Do not remove cited sources, data, or technical terms.

Phase 3: Vocabulary and Style Pass

Apply pattern-specific rewrites from the detection report:

• Replace AI-frequency vocabulary with natural alternatives
• Restore simple copulas (is/are/has) where the text uses elaborate substitutes
• Remove filler phrases and excessive hedging
• Cut promotional language and significance inflation
• Replace vague attributions with specific ones (or remove if no source exists)

Load the appropriate style profile from `references/style-guide.md` based on the target domain. Apply domain-specific voice calibration.

Phase 4: Entropy and Variation

Human writing has burstiness — irregular rhythm, varied sentence lengths, uneven information density. AI text is statistically smooth. This phase breaks that smoothness.

Load `references/statistical-signals.md` for target r