---

name: humanizer compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Detects and removes AI writing patterns to produce natural human writing through two-pass editing process license: MIT maturity: stable metadata: domain: writing output-format: analysis related-skills: code-review, markdown-best-practices role: review scope: review triggers: humanize text, remove AI writing, edit for natural, avoid AI patterns, write like human, writing edit, text review, code documentation write like human write like human archetypes:
  • educational anti_triggers:
  • brainstorming
  • vague ideation response_profile: verbosity: medium directive_strength: medium abstraction_level: tactical version: "1.0.0"

---

Humanizer: Remove AI Writing Patterns

Detects AI-generated writing patterns and transforms them into natural, human-style writing through a systematic two-pass editing process.

When to Use

Use this skill when:

• Editing AI-generated content to appear more human-written
• Preparing technical documentation for human audience
• Reviewing automated content for natural writing style
• Editing marketing copy to avoid AI detection
• Refining chatbot or LLM responses for more conversational tone
• Preparing user-facing messages that should sound human-written
• Editing any text that exhibits AI writing patterns

When NOT to Use

Avoid this skill for:

• Technical code comments that require precise terminology
• Legal or regulatory documents that need formal language
• Machine-readable output (JSON, YAML, configuration files)
• Code that intentionally uses AI patterns for clarity
• Technical specifications where formal tone is required
• Content that must maintain specific brand voice regardless of AI detection

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Core Workflow

The humanizer implements a two-pass editing process to systematically identify and replace AI patterns with natural alternatives.

Pass 1: Pattern Detection and Analysis

1. Load Reference Catalog — Read the complete pattern catalog from references/patterns.md. Checkpoint: All 24 patterns must be loaded with their signal words, categories, and examples.

2. Scan Text for Patterns — Systematically check the input text against all pattern signal words. Checkpoint: Generate a list of all detected pattern occurrences with positions and context.

3. Categorize Patterns — Group detected patterns by category (Content, Language, Style, Communication). Checkpoint: Ensure each pattern is classified correctly before proposing replacements.

4. Assess Context — For each detected pattern, analyze surrounding context to determine appropriate replacement. Checkpoint: Verify replacement maintains original meaning while sounding more natural.

Pass 2: Replacement and Validation

1. Propose Replacements — Generate natural alternatives for each detected pattern. Checkpoint: Each replacement must preserve meaning and improve naturalness.

2. Apply Edits — Replace AI patterns with human alternatives. Checkpoint: Track all changes with original and replacement text for audit trail.

3. Review Results — Read the humanized text aloud or to a colleague for feedback. Checkpoint: Ensure the result reads naturally and doesn't sound forced.

4. Final Validation — Run the humanized text through an AI detection tool to verify effectiveness. Checkpoint: AI detection score should drop below threshold (typically < 30%).

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Implementation Patterns

Pattern Detection Function (Python)

A practical implementation for detecting AI writing patterns in text:

import re
from typing import List, Dict, Tuple
from dataclasses import dataclass

@dataclass
class PatternMatch:
    """Represents a detected AI writing pattern."""
    pattern_id: int
    pattern_name: str
    signal_word: str
    context: str
    position: int

class AIPatternDetector:
    """Detects AI writing patterns in text."""
    
    def __init__(self, catalog: Dict[str, List[str]]):
        """Initialize detector with pattern catalog.
        
        Args:
            catalog: Dictionary mapping category to list of signal words
        """
        self.catalog = catalog
        self.patterns = self._build_patterns()
    
    def _build_patterns(self) -> List[Tuple[str, str]]:
        """Build regex patterns from catalog."""
        all_patterns = []
        for category, words in self.catalog.items():
            for word in words:
                # Create case-insensitive word boundary pattern
                pattern = re.compile(r'\b' + re.escape(word) + r'\b', re.IGNORECASE)
                all_patterns.append((pattern, category, word))
        return all_patterns
    
    def detect(self, text: str) -> List[PatternMatch]:
        """Scan text for AI writing patterns.
        
        Args:
            text: Input text to analyze
            
        Returns:
            List of detected pattern matches with context
        """
        matches = []
        for pattern, category, signal_word in self.patterns:
            for match in pattern.finditer(text):
                # Extract surrounding context (50 chars before and after)
                start = max(0, match.start() - 50)
                end = min(len(text), match.end() + 50)
                context = text[start:end].strip()
                
                matches.append(PatternMatch(
                    pattern_id=hash(signal_word) % 1000,
                    pattern_name=signal_word,
                    signal_word=signal_word,
                    context=context,
                    position=match.start()
                ))
        
        # Sort by position in text
        return sorted(matches, key=lambda m: m.position)
    
    def categorize(self, matches: List[PatternMatch]) -> Dict[str, List[PatternMatch]]:
        """Categorize detected patterns by category."""
        categories = {}
        for match in matches:
            # Get category from pattern lookup
            category = self._get_category(match.signal_word)
            if category not in categories:
                categories[category] = []
            categories[category].append(match)
        return categories
    
    def _get_category(self, signal_word: str) -> str:
        """Look up category for a signal word."""
        for category, words in self.catalog.items():
            if signal_word.lower() in [w.lower() for w in words]:
                return category
        return "unknown"

Pattern Replacement Function (Python)

A practical implementation for replacing AI patterns with human alternatives:

from typing import List, Dict, Tuple
import re

class AIPatternReplacer:
    """Replaces AI patterns with natural human alternatives."""
    
    def __init__(self, replacements: Dict[str, str]):
        """Initialize replacer with replacement mappings.
        
        Args:
            replacements: Dictionary mapping signal words to human alternatives
        """
        self.replacements = replacements
        # Build reverse lookup for faster matching
        self.patterns = self._build_replacement_patterns()
    
    def _build_replacement_patterns(self) -> List[Tuple[re.Pattern, str]]:
        """Build compiled regex patterns for replacements."""
        patterns = []
        for word, replacement in self.replacements.items():
            pattern = re.compile(r'\b' + re.escape(word) + r'\b', re.IGNORECASE)
            patterns.append((pattern, replacement))
        return patterns
    
    def replace(self, text: str, matches: List[PatternMatch]) -> Tuple[str, List[Dict]]:
        """Replace AI patterns with human alternatives.
        
        Args:
            text: Original text
            matches: Detected pattern matches to replace
            
        Returns:
            Tuple of (humanized text, change log)
        """
        # Sort matches by position in reverse order to replace from end to start
        sorted_matches = sorted(matches, key=lambda m: m.position, reverse=True)
        
        change_log = []
        result_text = text
        
        for match in sorted_matches:
            signal_word = match.signal_word
            # Look up replacement (use pattern name as key if exact match not found)
            replacement = self.replacements.get(signal_word.lower())
            
            if replacement:
                # Build replacement pattern
                pattern = re.compile(r'\b' + re.escape(signal_word) + r'\b', re.IGNORECASE)
                
                # Apply replacement
                new_text, count = pattern.subn(replacement, result_text, count=1)
                if count > 0:
                    change_log.append({
                        'original': signal_word,
                        'replacement': replacement,
                        'position': match.position,
                        'context': match.context
                    })
                    result_text = new_text
        
        return result_text, change_log
    
    def batch_replace(self, text: str) -> Tuple[str, List[Dict]]:
        """Replace all known patterns in text.
        
        Args:
            text: Input text
            
        Returns:
            Tuple of (humanized text, change log)
        """
        matches = []  # Would use detector to find matches first
        return self.replace(text, matches)

Example Usage

# Example usage of the humanizer

# Define pattern catalog (simplified)
catalog = {
    "Content": ["landmark", "pivotal", "monumental", "groundbreaking"],
    "Language": ["additionally", "crucially", "significantly", "utilize"],
    "Style": ["em dash", "passive voice", "overly long"],
    "Communication": ["over-explain", "defending obvious", "hedging"]
}

# Define replacement mappings
replacements = {
    "landmark": "important",
    "pivotal": "key",
    "monumental": "significant",
    "groundbreaking": "innovative",
    "additionally": "also",
    "crucially": "importantly",
    "significantly": "notably",
    "utilize": "use"
}

# Initialize components
detector = AIPatternDetector(catalog)
replacer = AIPatternReplacer(replacements)

# Process text
text = "This discovery is a landmark testament to years of dedicated research."
matches = detector.detect(text)
print(f"Detected {len(matches)} patterns:")
for match in matches:
    print(f"  - {match.signal_word} at position {match.position}")

# Apply replacements
humanized_text, changes = replacer.replace(text, matches)
print(f"\nHumanized: {humanized_text}")
print(f"Changes made: {len(changes)}")
for change in changes:
    print(f"  '{change['original']}' → '{change['replacement']}'")

Key Pattern Categories

1. Content Patterns (6 patterns) — Overuse of certain words and concepts
2. Language Patterns (6 patterns) — Words and phrases that sound robotic
3. Style Patterns (6 patterns) — Structural and formatting issues
4. Communication Patterns (6 patterns) — How ideas are expressed

For detailed pattern definitions, examples, and replacement strategies, see the pattern catalog.

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Constraints

MUST DO

• Always use the two-pass workflow (detection first, then replacement)
• Reference the complete pattern catalog for all pattern definitions
• Preserve original meaning in all replacements
• Track changes with original and replacement text for auditability
• Validate results with AI detection tools when possible
• Read results aloud to verify naturalness
• Consider context when choosing replacements (same meaning, better style)

MUST NOT DO

• Replace patterns that don't sound artificial in context
• Change technical terminology to sound more human
• Remove precision for the sake of naturalness
• Apply patterns mechanically without considering context
• Disable or bypass the two-pass process for "speed"
• Replace patterns that are intentional for brand voice
• Use the same replacement for every occurrence of a pattern

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Output Template

When applying this skill, produce:

1. Pattern Detection Report

   • List of all detected patterns with positions
   • Category classification for each pattern
   • Context for each detection

2. Replacement Proposals

   • Original text for each pattern occurrence
   • Proposed human alternative
   • Reason for the replacement

3. Humanized Text

   • Complete text with all replacements applied
   • Change log with original → replacement mappings

4. Validation Results

   • AI detection score before/after
   • Readability assessment
   • Any remaining patterns or concerns

5. Recommendations

   • Additional patterns to watch for
   • Style improvements for future writing
   • Words or phrases to avoid

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Related Skills

Skill	Purpose
code-review	Comprehensive code review with quality and security focus
markdown-best-practices	Markdown syntax rules and documentation practices for OpenCode skills

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This skill helps transform AI-generated text into natural, human-style writing by systematically detecting and replacing common AI writing patterns.

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

• Google Developer Writing Style Guide — Google's style guide for clear, concise technical writing with guidance on voice and tone
• Microsoft Writing Style Guide — Microsoft's documentation on writing naturally, avoiding AI-sounding patterns in technical prose
• Purdue OWL: Academic and Professional Writing — Purdue Online Writing Lab resources for improving clarity, voice, and human-like communication
• Hemingway Editor Guidelines — Hemingway's guidelines for readable writing with techniques to reduce AI-typical complexity markers
• The Elements of Style (Strunk & White) — Classic writing guide on concise, natural prose that avoids verbosity and artificial construction