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 ranges. Apply:
- Sentence length variance: mix short declarative with longer explanatory. Target visible variance across any 5-sentence window.
- Clause density variation: alternate simple sentences (one clause) with compound/complex (2-3 clauses). Do not settle on a uniform clause count.
- Information density variation: let some sentences carry heavy detail while others are light — a summary statement, a reaction, a pivot. Uniform density reads as generated.
- Phrase template breaking: replace stock collocations with specific phrasings. "Play a role in" -> name the specific action. "In terms of" -> delete or restructure.
- Inter-sentence cohesion variation: not every sentence should tightly follow the previous. Allow small topic expansions, brief asides, or contextual jumps that a thinking human would make.
- Function word personalization: vary connector usage. Use "but" in one place, "still" in another, nothing in a third. Do not default to the same conjunction pattern throughout.
- Paragraph length variance: mix single-sentence paragraphs with 4-5 sentence blocks.
- Controlled imperfection: fragments at impact positions, parenthetical asides, concessive turns. Sparingly — seasoning, not structure.
Phase 5: Validation and Output
Two checks before delivering:
Semantic check: Compare rewrite against original. Every factual claim, data point, argument, and technical term in the original must be present in the rewrite. If anything was lost, restore it.
Self-audit: Ask internally: "What still sounds AI-generated about this text?" If residual patterns remain, fix them. One pass only — do not loop indefinitely.
Output the final text followed by a brief changes summary.
Output Format
Full Rewrite / Targeted Fix / Style Shift
[Humanized text]
---
Changes: [2-4 bullet summary of what was changed and why]
Patterns detected: [list of pattern numbers/names found]
Domain: [detected or specified domain]For short texts (under 100 words), skip the changes summary unless the user requests it.
Detection Only
## Detection Report
**Domain:** [detected or specified]
**Overall severity:** [HIGH / MEDIUM / LOW]
**Patterns found:** [count]
### Findings
| Location | Pattern | Severity | Evidence |
|----------|---------|----------|----------|
| Para 1 | #7 AI vocabulary | HIGH | "delve", "intricate", "pivotal" in same sentence |
| Para 2 | #8 Copula avoidance | MEDIUM | "serves as" instead of "is" |
| Para 1-4 | Sentence length uniformity | MEDIUM | All sentences 18-22 words, SD < 3 |
| ... | ... | ... | ... |
### Statistical Signals
| Signal | Status | Detail |
|--------|--------|--------|
| Sentence length variance | FLAG | SD ~3 words (human typical: 7-15) |
| Transition frequency | OK | 5 per 1,000 words |
| ... | ... | ... |
### Summary
[1-2 sentences: overall assessment and highest-priority patterns to fix first]Reference Files
| File | Purpose | Load When |
|---|---|---|
references/detection-patterns.md | 24 AI-writing patterns with examples | Always (Phase 1) |
references/statistical-signals.md | 12 statistical regularity signals with target ranges | Phase 1 (scan) and Phase 4 (targets) |
references/style-guide.md | Domain-specific voice profiles and calibration rules | Phase 3 (match to domain) |
references/transformation-rules.md | Structural rewrite strategies and entropy techniques | Phase 2 and Phase 4 |
examples/academic.md | Before/after pairs for academic writing | When domain is academic |
examples/blog.md | Before/after pairs for blog/casual writing | When domain is blog or social |
examples/professional.md | Before/after pairs for professional/business writing | When domain is professional |
Domain Detection
If the user does not specify a domain, infer from:
- Vocabulary density and jargon type
- Citation patterns
- Sentence complexity
- Register (formal/informal markers)
Default to professional if ambiguous.
Supported domains: academic, technical, blog, social, professional, marketing
Behavioral Constraints
- Never fabricate. Do not add facts, citations, quotes, statistics, or claims not in the original.
- Never remove data. Numbers, dates, names, URLs, and cited sources must survive the rewrite.
- Preserve argument structure. If the original makes points A, B, C in that order with that logic, the rewrite must preserve the logical flow.
- Do not over-humanize. Some text is meant to be neutral and informational. A technical specification does not need personality. Match the appropriate register.
- Respect code blocks and structured data. Do not humanize code, tables, JSON, YAML, or any structured/machine-readable content. Pass these through unchanged.
- One pass through the pipeline. Do not run the 5-phase pipeline recursively. If the output still has tells after Phase 5, note them in the changes summary rather than looping.
Scope Modes
| Mode | Trigger | Behavior |
|---|---|---|
| Full rewrite | "humanize this", "rewrite naturally" | Run all 5 phases |
| Detection only | "check for AI patterns", "does this sound AI" | Run Phase 1 only, output detection report |
| Targeted fix | "fix the AI-sounding parts", "just clean up the obvious stuff" | Run Phase 1, then apply fixes only to HIGH-priority patterns |
| Style shift | "make this more casual/academic/professional" | Run Phases 3-4 with specified domain profile |
Error Handling
| Problem | Cause | Resolution |
|---|---|---|
| Input under 20 words | Insufficient signal for pattern detection | Report: "Text too short for reliable pattern detection." Apply vocabulary fixes only (Phase 3) if obvious patterns are present. Skip statistical signal analysis. |
| Input is entirely code/structured data | No prose to humanize | Report: "Input is structured data — no humanization applicable." Return input unchanged. |
| Mixed human + AI text | Partial AI generation or human-edited AI output | Run Phase 1 on full text. Flag only paragraphs/sections with detected patterns. Apply Phases 2-4 selectively to flagged sections. Leave clean sections untouched. |
| Domain ambiguous after detection | Input mixes registers (e.g., academic citations in a blog post) | Default to professional. Note the ambiguity in the output: "Domain defaulted to professional — specify if another profile is preferred." |
| Semantic drift detected in Phase 5 | Rewrite altered meaning during structural/vocabulary changes | Restore the drifted factual claim from the original. Do not re-run the full pipeline. Note the restoration in the changes summary. |
| Input contains fabricated citations | Original text has hallucinated sources | Not detectable — this skill humanizes style, not factual accuracy. Pass through unchanged. Note in limitations if the user asks about accuracy. |
| All patterns are LOW severity | Text is mostly human-written with minor tells | In targeted fix mode, report findings but recommend no changes. In full rewrite mode, apply light-touch fixes only — do not over-edit clean text. |
Integration Point
Other writing skills can import references/detection-patterns.md as a pattern library for their own anti-pattern sweeps. The detection patterns are the shared asset; the pipeline is this skill's domain.
Limitations
- Cannot verify factual accuracy of the original text. Garbage in, humanized garbage out.
- Effectiveness depends on input length. Very short texts (under 20 words) have insufficient signal for pattern detection.
- Style profiles are guidelines, not voice cloning. The output will sound natural but will not match a specific author's voice without additional calibration.
- Does not interact with external AI-detection APIs. Assessment is heuristic, not benchmark-verified.