Anti-Slop Toolkit

When to Use This Skill

Use anti-slop-toolkit when:

• ✓ Running automated slop detection across a codebase
• ✓ Integrating slop checks into CI/CD pipelines
• ✓ Setting up pre-commit hooks for quality enforcement
• ✓ Auditing large projects for generic AI patterns
• ✓ Batch processing multiple files for cleanup
• ✓ Establishing quantitative quality thresholds

Do NOT use when:

• Manual review is more appropriate (use domain-specific anti-slop skills)
• Dealing with non-text files (images, binaries)
• Working with languages not covered by scripts (use manual skills)

Quick Example

Running Detection:

# Text file detection
$ python scripts/detect_slop.py report.md --verbose

Analyzing: report.md
Overall Slop Score: 45/100

High-Risk Phrases (3 found):
  Line 12: "delve into the complexities"
  Line 34: "in today's fast-paced world"
  Line 67: "it's important to note that"

Recommendations:
- Remove meta-commentary
- Simplify wordy phrases
- Replace buzzwords with direct language

# R code detection
$ Rscript scripts/detect_slop.R analysis.R --verbose

Analyzing: analysis.R
Overall Slop Score: 52/100

Generic Variables (5 found):
  Line 8: df
  Line 15: data
  Line 23: result

Recommendations:
- Replace generic variable names
- Add namespace qualification
- Remove obvious comments

Running Cleanup:

# Preview changes
$ python scripts/clean_slop.py report.md

Would remove:
  Line 12: "delve into the complexities" → "examine"
  Line 34: "in today's fast-paced world" → [deleted]

# Apply changes (creates backup)
$ python scripts/clean_slop.py report.md --save

Backup created: report.md.backup
Cleaned: report.md
Removed 15 slop patterns

When to Use What

Core Workflow

3-Step Automated Quality Check

1. Run detection to assess scope

   bashCopy

   # Single file
   python scripts/detect_slop.py document.md --verbose
   
   # Multiple files
   find . -name "*.md" -exec python scripts/detect_slop.py {} \;
   

2. Review findings and set thresholds

   bashCopy

   # Acceptable: score < 30
   # Needs work: score 30-50
   # Must fix: score > 50
   

3. Apply automated cleanup where safe

   bashCopy

   # Conservative cleanup (preserves meaning)
   python scripts/clean_slop.py document.md --save
   
   # Aggressive cleanup (may change nuance)
   python scripts/clean_slop.py document.md --save --aggressive
   

Quick Reference Checklist

For CI/CD integration:

• Scripts installed in project (copy from toolkit/scripts/)
• Detection thresholds defined (e.g., fail if score > 40)
• File types to check specified
• Exceptions documented (files that can't be cleaned)
• Backup strategy for cleanup operations
• Exit codes configured for pipeline failures

For manual use:

• Run detection first (never blind cleanup)
• Review verbose output for context
• Test cleanup on copy before applying
• Verify cleaned output maintains meaning
• Commit cleaned files separately for easy review

Common Workflows

Workflow 1: Audit Entire Codebase

Context: Review project for generic AI patterns before release.

Steps:

1. Run comprehensive detection

   bashCopy

   #!/bin/bash
   # audit_slop.sh
   
   echo "=== Text Files ==="
   find . -name "*.md" -o -name "*.txt" | while read file; do
     score=$(python scripts/detect_slop.py "$file" | grep "Score:" | awk '{print $3}')
     echo "$file: $score"
   done
   
   echo ""
   echo "=== R Files ==="
   find . -name "*.R" | while read file; do
     score=$(Rscript scripts/detect_slop.R "$file" | grep "Score:" | awk '{print $3}')
     echo "$file: $score"
   done
   

2. Generate summary report

   bashCopy

   # Count files by severity
   echo "Files by severity:"
   echo "Low (0-20): $(grep -c "1[0-9]/100\|[0-9]/100" audit.log)"
   echo "Moderate (20-40): $(grep -c "[23][0-9]/100" audit.log)"
   echo "High (40-60): $(grep -c "[45][0-9]/100" audit.log)"
   echo "Severe (60+): $(grep -c "6[0-9]/100\|7[0-9]/100\|8[0-9]/100\|9[0-9]/100" audit.log)"
   

3. Prioritize high-severity files

   bashCopy

   # List files with score > 50
   grep -E "[5-9][0-9]/100" audit.log | sort -t: -k2 -rn > high_priority.txt
   

4. Apply cleanup to high-priority files

   bashCopy

   # Clean files with score > 50
   while read line; do
     file=$(echo $line | cut -d: -f1)
     python scripts/clean_slop.py "$file" --save
   done < high_priority.txt
   

5. Verify improvements

   bashCopy

   # Re-run detection on cleaned files
   while read line; do
     file=$(echo $line | cut -d: -f1)
     python scripts/detect_slop.py "$file"
   done < high_priority.txt
   

Expected outcome: Prioritized cleanup plan with before/after scores

Workflow 2: CI/CD Pipeline Integration

Context: Enforce quality standards in continuous integration.

Steps:

1. Create detection script for CI

   bashCopy

   #!/bin/bash
   # ci_check_slop.sh
   
   THRESHOLD=40
   FAILED=0
   
   # Check markdown files
   for file in $(find . -name "*.md"); do
     score=$(python scripts/detect_slop.py "$file" | grep -oP "Score: \K\d+")
     if [ "$score" -gt "$THRESHOLD" ]; then
       echo "FAIL: $file (score: $score, threshold: $THRESHOLD)"
       FAILED=1
     else
       echo "PASS: $file (score: $score)"
     fi
   done
   
   # Check R files
   for file in $(find . -name "*.R"); do
     score=$(Rscript scripts/detect_slop.R "$file" | grep -oP "Score: \K\d+")
     if [ "$score" -gt "$THRESHOLD" ]; then
       echo "FAIL: $file (score: $score, threshold: $THRESHOLD)"
       FAILED=1
     fi
   done
   
   exit $FAILED
   

2. Add to GitHub Actions

   yamlCopy

   # .github/workflows/quality-check.yml
   name: Quality Check
   
   on: [pull_request]
   
   jobs:
     anti-slop:
       runs-on: ubuntu-latest
       steps:
         - uses: actions/checkout@v3
         - name: Set up Python
           uses: actions/setup-python@v4
           with:
             python-version: '3.x'
         - name: Set up R
           uses: r-lib/actions/setup-r@v2
         - name: Check for slop
           run: |
             chmod +x ci_check_slop.sh
             ./ci_check_slop.sh
   

3. Configure for GitLab CI

   yamlCopy

   # .gitlab-ci.yml
   quality_check:
     stage: test
     image: rocker/r-ver:latest
     before_script:
       - apt-get update && apt-get install -y python3
     script:
       - chmod +x ci_check_slop.sh
       - ./ci_check_slop.sh
     allow_failure: false
   

4. Add quality gate

   bashCopy

   # Only fail on severe slop (score > 60)
   THRESHOLD=60
   # Warn on moderate slop (score 40-60) but don't fail
   WARN_THRESHOLD=40
   

5. Generate CI report

   bashCopy

   # Add to CI script
   if [ "$score" -gt "$THRESHOLD" ]; then
     echo "::error file=$file::Slop score $score exceeds threshold $THRESHOLD"
   elif [ "$score" -gt "$WARN_THRESHOLD" ]; then
     echo "::warning file=$file::Slop score $score exceeds warning threshold"
   fi
   

Expected outcome: Automated quality enforcement in pull requests

Workflow 3: Pre-commit Hook Setup

Context: Prevent slop from entering repository with pre-commit hooks.

Steps:

1. Create pre-commit hook

   bashCopy

   #!/bin/bash
   # .git/hooks/pre-commit
   
   # Get staged files
   STAGED_MD=$(git diff --cached --name-only --diff-filter=ACM | grep "\.md$")
   STAGED_R=$(git diff --cached --name-only --diff-filter=ACM | grep "\.R$")
   
   THRESHOLD=50
   ISSUES=0
   
   # Check markdown files
   for file in $STAGED_MD; do
     if [ -f "$file" ]; then
       score=$(python scripts/detect_slop.py "$file" | grep -oP "Score: \K\d+")
       if [ "$score" -gt "$THRESHOLD" ]; then
         echo "ERROR: $file has slop score $score (threshold: $THRESHOLD)"
         ISSUES=1
       fi
     fi
   done
   
   # Check R files
   for file in $STAGED_R; do
     if [ -f "$file" ]; then
       score=$(Rscript scripts/detect_slop.R "$file" | grep -oP "Score: \K\d+")
       if [ "$score" -gt "$THRESHOLD" ]; then
         echo "ERROR: $file has slop score $score (threshold: $THRESHOLD)"
         ISSUES=1
       fi
     fi
   done
   
   if [ $ISSUES -ne 0 ]; then
     echo ""
     echo "Commit rejected: Files exceed slop threshold"
     echo "Run 'python scripts/clean_slop.py <file> --save' to clean"
     echo "Or use 'git commit --no-verify' to bypass (not recommended)"
     exit 1
   fi
   
   exit 0
   

2. Make hook executable

   bashCopy

   chmod +x .git/hooks/pre-commit
   

3. Test hook

   bashCopy

   # Create test file with slop
   echo "It's important to note that we will delve into..." > test.md
   git add test.md
   git commit -m "Test"
   # Should fail with error message
   

4. Add auto-fix option

   bashCopy

   # Add to hook after detecting issues
   echo ""
   echo "Attempt auto-fix? (y/n)"
   read -r response
   if [ "$response" = "y" ]; then
     for file in $STAGED_MD; do
       python scripts/clean_slop.py "$file" --save
       git add "$file"
     done
     echo "Files cleaned and re-staged"
     exit 0
   fi
   

5. Share hook with team

   bashCopy

   # Create shared hooks directory
   mkdir -p .githooks
   cp .git/hooks/pre-commit .githooks/
   
   # Configure git to use shared hooks
   git config core.hooksPath .githooks
   
   # Document in README
   echo "Run: git config core.hooksPath .githooks" >> README.md
   

Expected outcome: Automatic slop prevention before commits

Script Reference

detect_slop.py

Purpose: Analyze text files for AI slop patterns.

Usage:

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

What it detects:

• High-risk phrases (delve, navigate complexities, fast-paced world)
• Wordy constructions (in order to, due to the fact that)
• Meta-commentary (it's important to note)
• Buzzwords (leverage, synergistic, paradigm)
• Excessive hedging (might possibly, could potentially)

Output:

Overall Slop Score: 45/100

High-Risk Phrases (3):
  Line 12: "delve into"
  Line 34: "in today's world"

Recommendations:
- Remove meta-commentary
- Simplify wordy phrases

Scoring:

• 0-20: Low slop (authentic)
• 20-40: Moderate slop (some patterns)
• 40-60: High slop (many patterns)
• 60+: Severe slop (heavily generic)

clean_slop.py

Purpose: Automatically remove common slop patterns from text.

Usage:

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

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

# Output to different file
python scripts/clean_slop.py <file> --output cleaned.txt

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

What it cleans:

• High-risk phrases → direct alternatives
• Wordy constructions → concise equivalents
• Meta-commentary → deleted
• Buzzwords → plain language
• Redundant qualifiers → removed
• Empty intensifiers → deleted

Safety:

• Always creates .backup file when using --save
• Preview mode shows all changes before applying
• Conservative by default (preserves meaning)
• Aggressive mode removes more but may alter nuance

Example transformations:

Before: "It's important to note that we will delve into..."
After: "We examine..."

Before: "In order to understand this, due to the fact that..."
After: "To understand this, because..."

Before: "Leverage synergistic paradigms..."
After: "Use cooperative approaches..."

detect_slop.R

Purpose: Analyze R code files for AI slop patterns.

Usage:

# Single file
Rscript scripts/detect_slop.R <file.R> [--verbose]

# Directory
Rscript scripts/detect_slop.R <directory> [--verbose]

# Package R/ folder
Rscript scripts/detect_slop.R mypackage/R/

What it detects:

• Generic variable names (df, data, temp, result, res, out)
• Generic function names (process_, do_, helper)
• Obvious comments ("Load the library", "Filter the data")
• Unnecessary single pipes (x %>% f() instead of f(x))
• Overly long pipe chains (>8 operations)
• Generic roxygen documentation (@param data The data)

Output:

Analyzing: analysis.R
Overall Slop Score: 52/100

Generic Variables (5):
  Line 8: df
  Line 15: data
  Line 23: result

Generic Function Names (2):
  Line 45: process_data()
  Line 78: helper()

Obvious Comments (4):
  Line 5: "# Load the library"
  Line 12: "# Filter the data"

Recommendations:
- Replace generic variable names with descriptive ones
- Add namespace qualification (use ::)
- Remove obvious comments
- Simplify single-pipe constructs

Scoring:

• 0-20: Low slop (thoughtful R code)
• 20-40: Moderate slop (some generic patterns)
• 40-60: High slop (many AI patterns)
• 60+: Severe slop (heavily generic code)

Best Practices

When to Use Automation

Good use cases:

• Large codebases with many files
• Consistent pattern enforcement across team
• Pre-release quality audits
• Continuous integration checks
• Batch cleanup of known patterns

Poor use cases:

• Nuanced writing requiring context
• Academic papers with domain conventions
• Code with intentional generic names (e.g., data in function examples)
• First-time quality review (start manual)

Setting Thresholds

Conservative thresholds (strict standards):

• Fail: score > 30
• Warn: score > 20
• Good for: Public releases, documentation, client deliverables

Moderate thresholds (balanced):

• Fail: score > 50
• Warn: score > 30
• Good for: Internal code, team projects, iterative work

Lenient thresholds (pragmatic):

• Fail: score > 70
• Warn: score > 50
• Good for: Exploratory work, drafts, legacy code

Cleanup Strategy

Always:

1. Run detection first (understand scope)
2. Preview cleanup changes (review transformations)
3. Create backups (use --save for automatic backup)
4. Test output (verify meaning preserved)
5. Commit separately (easy to review/revert)

Never:

• Blind batch cleanup without review
• Aggressive mode on important documents
• Skip manual verification of automated changes
• Apply to files with domain-specific conventions

Integration Patterns

With Git Workflow

# Before committing
git diff --name-only | grep "\.md$" | while read file; do
  python scripts/detect_slop.py "$file"
done

# Clean before commit
python scripts/clean_slop.py changed_file.md --save
git add changed_file.md
git commit -m "Clean slop from documentation"

With Make/Build System

# Makefile
.PHONY: check-slop clean-slop

check-slop:
	@find . -name "*.md" | while read f; do \
		python scripts/detect_slop.py "$$f"; \
	done

clean-slop:
	@find . -name "*.md" | while read f; do \
		python scripts/clean_slop.py "$$f" --save; \
	done

# Usage: make check-slop

With R Package Development

# R/zzz.R
check_slop <- function(path = "R/") {
  r_files <- list.files(path, pattern = "\\.R$", full.names = TRUE)
  lapply(r_files, function(f) {
    system(paste("Rscript scripts/detect_slop.R", f))
  })
}

# Usage: check_slop()

Limitations

Script Coverage

detect_slop.py:

• Text files only (.md, .txt)
• English language optimized
• Can't understand all contexts
• May flag acceptable domain conventions

detect_slop.R:

• R code files only (.R)
• Doesn't detect all R-specific patterns
• May flag intentional generic names in examples
• Doesn't check roxygen completeness (just generic patterns)

clean_slop.py:

• Text files only
• May alter meaning in aggressive mode
• Can't handle all edge cases
• Requires manual review

Manual Skills for Other Cases

• Python code: Use python/anti-slop skill (manual)
• Design/visualization: Use design/anti-slop skill (manual)
• Julia/C++ code: Use julia/anti-slop or cpp/anti-slop (manual)
• Complex documentation: Use text/anti-slop skill (manual)

Context Sensitivity

Scripts can't understand:

• Domain-specific conventions
• Acceptable generic names in specific contexts
• Intentional stylistic choices
• Academic writing requirements
• Legal/regulatory language needs

Always combine automated detection with manual review.

Troubleshooting

Script Not Found

# Ensure scripts directory exists
ls toolkit/scripts/

# Should see:
# detect_slop.py
# clean_slop.py
# detect_slop.R

Python Dependencies

# Scripts use only standard library
# No dependencies needed
python3 scripts/detect_slop.py --help

R Script Fails

# Ensure R is installed
which Rscript

# Scripts use only base R
# No packages needed
Rscript scripts/detect_slop.R --help

Permissions

# Make scripts executable
chmod +x scripts/*.py
chmod +x scripts/*.R

High False Positive Rate

• Adjust thresholds (increase threshold values)
• Use manual review for edge cases
• Whitelist specific files in CI scripts
• Document acceptable exceptions

Resources & Advanced Topics

Script Locations

All scripts in toolkit/scripts/:

• detect_slop.py - Text detection
• clean_slop.py - Text cleanup
• detect_slop.R - R code detection

Related Skills

• r/anti-slop - Manual R code review
• python/anti-slop - Manual Python code review
• text/anti-slop - Manual text review
• anti-slop - Meta-skill coordinator

Extending the Toolkit

Add new pattern detection:

# In detect_slop.py
new_patterns = [
    "your custom pattern",
    "another pattern"
]

Add new cleanup rules:

# In clean_slop.py
cleanup_rules = [
    ("old phrase", "replacement"),
    ("another old", "another new")
]

Create language-specific detector:

# Model on detect_slop.R structure
# Define patterns for your language
# Score based on pattern frequency

Integration with Domain Skills

This toolkit provides automated detection and cleanup.

Use together with domain skills for complete coverage:

Key distinction:

• Toolkit provides automated detection/cleanup
• Domain skills provide context and manual refinement

Both are complementary equals for maintaining quality standards.