Skills Duplicate Detector

Expert skill for identifying duplicate, similar, and overlapping Claude Code skills across your collection using intelligent comparison algorithms.

Purpose

Analyze a collection of Claude Code skills to identify duplicates, near-duplicates, and functionally similar skills. Helps maintain a clean, organized skills collection by detecting redundancy and suggesting consolidation.

When to Use

• After bulk downloading skills from multiple sources
• Maintaining an existing skills collection
• Before committing new skills to a repository
• Auditing skill collections for redundancy

Capabilities

1. Duplicate Detection Methods

#### Exact Name Matching

• Identify skills with identical names
• Detect name variations with prefixes/suffixes
• Find skills differing only in case

#### Semantic Similarity

• Analyze SKILL.md descriptions
• Compare stated purposes and capabilities
• Identify functional overlap
• Calculate similarity scores

#### Content Analysis

• Compare skill file structures
• Analyze included scripts and tools
• Check for shared dependencies
• Detect copy/paste variations

2. Similarity Categories

#### Perfect Duplicates (100% match)

• Identical names and content
• Same functionality and purpose
• Likely from same source

#### Near Duplicates (80-99% match)

• Similar names with minor variations
• Nearly identical functionality
• Possible version differences

#### Functional Overlaps (60-79% match)

• Different names, similar purpose
• Overlapping capabilities
• May serve different use cases

#### Related Skills (40-59% match)

• Adjacent functionality
• Complementary features
• Worth noting but not duplicates

3. Analysis Outputs

#### Duplicate Report

# Duplicate Skills Analysis

## Perfect Duplicates (7 found)

### Group 1: Code Review
- **code-review_mrgoonie** (KEEP - most comprehensive)
  - Location: /skills/code-review_mrgoonie
  - Size: 15 KB
  - Features: Requesting, receiving, verification

- **receiving-code-review_obra** (DUPLICATE)
  - Location: /skills/receiving-code-review_obra
  - Similarity: 95%
  - Recommendation: Move to /duplicates

- **requesting-code-review_obra** (DUPLICATE)
  - Location: /skills/requesting-code-review_obra
  - Similarity: 92%
  - Recommendation: Move to /duplicates

**Reason**: All three provide code review functionality. mrgoonie version combines both requesting and receiving in one comprehensive skill.

Usage Instructions

Basic Duplicate Detection

Use the skills-duplicate-detector skill to analyze /skills directory and identify all duplicates.

Detailed Analysis

Use the skills-duplicate-detector skill to:
1. Scan all skills in /skills directory
2. Identify duplicates with 70%+ similarity
3. Generate a detailed report with recommendations
4. Create a DUPLICATES_LOG.md file

Pre-commit Check

Before committing new skills, use skills-duplicate-detector to check if they already exist in the collection.

Detection Algorithm

Phase 1: Name Analysis

# Normalize skill names
def normalize_name(skill_name):
    # Remove source suffixes (_obra, _mrgoonie, etc.)
    # Convert to lowercase
    # Remove special characters
    # Standardize spacing
    return normalized_name

# Find exact and near matches
for skill1, skill2 in pairs:
    name_similarity = levenshtein_distance(
        normalize_name(skill1),
        normalize_name(skill2)
    )

Phase 2: Content Analysis

# Compare SKILL.md descriptions
def compare_descriptions(skill1, skill2):
    # Extract purpose statements
    # Tokenize and compare keywords
    # Calculate semantic similarity
    # Weight by capability overlap
    return similarity_score

Phase 3: Structural Analysis

# Compare file structures
def compare_structure(skill1, skill2):
    # List files and directories
    # Compare organization
    # Check for shared patterns
    # Identify clones vs. variations
    return structure_score

Comparison Heuristics

Name-Based Detection

• Exact match: Same name after normalization → 100%
• Prefix/suffix variation: skill vs skill_source → 95%
• Hyphen/underscore: code-review vs code_review → 90%
• Abbreviations: db-helper vs database-helper → 80%

Purpose-Based Detection

• Identical purpose: Same goals stated → 95%
• Overlapping functionality: 80%+ feature overlap → 85%
• Complementary: Different aspects of same domain → 60%
• Related: Adjacent problem spaces → 40%

Quality Assessment

When duplicates are found, recommend keeping the version with:

1. ✅ Most comprehensive documentation
2. ✅ Largest feature set
3. ✅ Most recent updates
4. ✅ Better code quality
5. ✅ More complete examples

Output Formats

Console Report

Duplicate Detection Results
===========================

Total skills scanned: 161
Unique skills: 154
Duplicates found: 7
Similarity threshold: 70%

Perfect Duplicates (3 groups):
  - Code review skills (3 items)
  - Debugging skills (2 items)
  - Skill creation tools (3 items)

Near Duplicates (2 groups):
  - Brainstorming skills (2 items)
  - Document skills (2 items)

Recommendations:
  - Move 7 skills to /duplicates
  - Keep 154 unique skills in /skills
  - Estimated disk space saved: 2.3 MB

Markdown Report (DUPLICATES_LOG.md)

• Detailed analysis of each duplicate group
• Side-by-side comparison tables
• Recommendations with reasoning
• Links to source repositories

JSON Export

{
  "analysis_date": "2025-11-07",
  "total_skills": 161,
  "unique_skills": 154,
  "duplicates": [
    {
      "group_id": 1,
      "group_name": "code-review",
      "members": [
        {
          "path": "/skills/code-review_mrgoonie",
          "keep": true,
          "reason": "Most comprehensive"
        },
        {
          "path": "/skills/receiving-code-review_obra",
          "keep": false,
          "similarity": 0.95
        }
      ]
    }
  ]
}

Configuration Options

Similarity Thresholds

thresholds:
  perfect_duplicate: 0.95      # 95%+ similarity
  near_duplicate: 0.80         # 80-94% similarity
  functional_overlap: 0.60     # 60-79% similarity
  related: 0.40                # 40-59% similarity
  report_minimum: 0.60         # Only report 60%+ matches

Comparison Weights

weights:
  name_similarity: 0.30        # 30% weight on name matching
  purpose_similarity: 0.40     # 40% weight on purpose/description
  structure_similarity: 0.20   # 20% weight on file structure
  feature_overlap: 0.10        # 10% weight on feature comparison

Output Options

output:
  console_report: true
  markdown_report: true
  json_export: false
  auto_move_duplicates: false  # Require confirmation
  create_backups: true         # Before moving files

Detection Patterns

Common Duplicate Patterns

1. Source-Suffixed Variants

   skill-name_obra
   skill-name_mrgoonie
   skill-name_diet103

1. Naming Convention Differences

   code-review
   code_review
   codeReview

1. Scope Variations

   document-skills         (broad)
   scientific-document-skills  (specialized)
   pdf-document-skills     (narrow)

1. Feature Splits

   code-review             (combined)
   requesting-code-review  (split)
   receiving-code-review   (split)

Special Cases

Not Duplicates

• Specialized vs. General: Scientific brainstorming vs. general brainstorming
• Different Frameworks: React testing vs. Vue testing
• Language-Specific: Python debugging vs. JavaScript debugging
• Platform-Specific: AWS deployment vs. GCP deployment

Borderline Cases

• Related Workflows: Git branching vs. Git worktrees
• Tool Variations: Docker basics vs. Docker Compose
• Complementary: API client vs. API server

Integration with Other Skills

Works Well With

• bulk-github-skills-downloader: Detect duplicates after download
• skills-consolidator: Merge similar skills
• git-workflow-helper: Commit duplicate cleanup
• skill-creator_mrgoonie: Check before creating new skills

Workflow

1. Scan Phase

• Enumerate all skills in /skills
• Read SKILL.md files
• Extract metadata and features

1. Analysis Phase

• Calculate pairwise similarities
• Group related skills
• Score and rank matches

1. Recommendation Phase

• Identify best version to keep
• Suggest duplicates to move
• Generate reasoning

1. Action Phase

• Create DUPLICATES_LOG.md
• Optionally move duplicates
• Update inventory

Best Practices

1. Review Before Moving: Always review recommendations before moving files
2. Preserve History: Keep duplicates in /duplicates, don't delete
3. Document Decisions: Record why certain skills were kept
4. Regular Audits: Run detector quarterly on growing collections
5. Version Awareness: Consider that "duplicates" might be different versions

Performance

• Speed: ~10-20 skills per second
• Memory: Minimal (streaming analysis)
• Accuracy: 95%+ for exact duplicates, 80%+ for near duplicates

Limitations

• Cannot detect functional duplicates with completely different implementations
• Requires SKILL.md files for semantic analysis
• May miss duplicates in non-standard formats
• Similarity scores are heuristic-based

Advanced Usage

Custom Similarity Rules

Detect duplicates but treat these as separate:
- Python-specific vs. JavaScript-specific skills
- Scientific vs. general-purpose variations
- Enterprise vs. open-source tool integrations

Bulk Cleanup

Use skills-duplicate-detector to:
1. Find all duplicates above 90% similarity
2. Automatically keep the most comprehensive version
3. Move others to /duplicates
4. Generate cleanup report
5. Commit changes with detailed message

Version: 1.0.0 Last Updated: 2025-11-07 Maintained by: @yourusername