anti-slop

Comprehensive toolkit for detecting and eliminating "AI slop" - generic, low-quality AI-generated patterns in natural language, code, and design. Use when reviewing or improving content quality, preventing generic AI patterns, cleaning up existing content, or enforcing quality standards in writing, code, or design work.

Anti-Slop Skill

Detect and eliminate generic AI-generated patterns ("slop") across natural language, code, and design.

What is AI Slop?

AI slop refers to telltale patterns that signal low-quality, generic AI-generated content:

This skill helps identify and remove these patterns to create authentic, high-quality content.

When to Use This Skill

Apply anti-slop techniques when:

Core Workflow

1. Detect Slop

For text files:

python scripts/detect_slop.py <file> [--verbose]

This analyzes text and provides:

Manual detection: Read the appropriate reference file for detailed patterns:

2. Clean Slop

Automated cleanup (text only):

# Preview changes
python scripts/clean_slop.py <file>

# Apply changes (creates backup)
python scripts/clean_slop.py <file> --save

# Aggressive mode (may slightly change meaning)
python scripts/clean_slop.py <file> --save --aggressive

Manual cleanup: Apply strategies from the reference files based on detected patterns.

Text Slop Detection & Cleanup

High-Priority Targets

Remove immediately:

Simplify wordy phrases:

Replace buzzwords:

Quality Principles

Be direct:

Be specific:

Be authentic:

Code Slop Detection & Cleanup

High-Priority Targets

Rename generic variables:

Remove obvious comments:

# Bad
# Create a user
user = User()

# Better - let code speak
user = User()

Simplify over-engineered code:

Improve function names:

Quality Principles

Clarity over cleverness:

Meaningful names:

Appropriate documentation:

Design Slop Detection & Cleanup

High-Priority Targets

Visual slop:

Layout slop:

Copy slop:

Quality Principles

Content-first design:

Intentional choices:

Authentic voice:

Reference Files

Consult these comprehensive guides when working on specific domains:

Each reference includes:

Scripts

detect_slop.py

Analyzes text files for AI slop patterns.

Usage:

python scripts/detect_slop.py <file> [--verbose]

Output:

Scoring:

clean_slop.py

Automatically removes common slop patterns from text files.

Usage:

# Preview changes
python scripts/clean_slop.py <file>

# Save changes (creates backup)
python scripts/clean_slop.py <file> --save

# Save to different file
python scripts/clean_slop.py <file> --output clean_file.txt

# Aggressive mode
python scripts/clean_slop.py <file> --save --aggressive

What it cleans:

Safety:

Best Practices

Prevention Over Cure

When creating content:

  1. Write with specific audience in mind
  2. Use concrete examples over abstractions
  3. Lead with the point, skip preambles
  4. Choose words for precision, not impression
  5. Review before considering it complete

Context-Aware Cleanup

Not all patterns are always slop:

Acceptable contexts:

Always consider:

Iterative Improvement

  1. Detect - Run detection scripts or manual review
  2. Analyze - Understand which patterns are truly problems
  3. Clean - Apply automated cleanup where safe
  4. Review - Manually verify changes maintain meaning
  5. Refine - Fix remaining issues by hand

Quality Over Automation

The scripts are tools, not replacements for judgment:

Integration Patterns

Code Review

# Check files before committing
python scripts/detect_slop.py src/documentation.md --verbose

# Clean up automatically
python scripts/clean_slop.py src/documentation.md --save

Content Pipeline

  1. Create initial content
  2. Run slop detection
  3. Apply automated cleanup
  4. Manual review and refinement
  5. Final quality check

Standards Enforcement

Create project-specific thresholds:

Limitations

Scripts only handle text:

Context sensitivity:

Language coverage:

Common Scenarios

Scenario 1: Review AI-Generated Content

# User asks: "Can you review this article for AI slop?"
1. Read references/text-patterns.md for patterns to watch
2. Run: python scripts/detect_slop.py article.txt --verbose
3. Review findings and apply manual cleanup
4. Optionally run: python scripts/clean_slop.py article.txt --save
5. Do final manual review of cleaned content

Scenario 2: Clean Up Codebase

# User asks: "Help me clean up generic AI patterns in my code"
1. Read references/code-patterns.md
2. Review code files manually for patterns
3. Create list of generic names to rename
4. Refactor following principles in code-patterns.md
5. Remove obvious comments and over-abstractions

Scenario 3: Design Review

# User asks: "Does this design look too generic?"
1. Read references/design-patterns.md
2. Check against high-confidence slop indicators
3. Identify specific issues (gradients, layouts, copy)
4. Provide specific recommendations from design-patterns.md
5. Suggest concrete alternatives

Scenario 4: Establish Quality Standards

# User asks: "Help me create quality standards for our team"
1. Review all three reference files
2. Identify patterns most relevant to user's domain
3. Create project-specific guidelines
4. Set up detection scripts in development pipeline
5. Document acceptable exceptions

Tips for Success

For text cleanup:

For code cleanup:

For design cleanup:

General principles: