๐ŸŽ Get the FREE AI Skills Starter Guide โ€” Subscribe โ†’
BytesAgainBytesAgain
BytesAgain โ€บ Skills โ€บ Plagiarism Checker
๐Ÿฆ€ ClawHub

Plagiarism Checker

by @aipoch-ai

Use when: User provides text/document and asks to check originality, detect plagiarism, assess similarity, or rewrite high-duplicate content. Triggers: "chec...

Versionv1.0.0
Downloads715
#clawhub
TERMINAL
clawhub install plagiarism-checker-pre-screener

๐Ÿ“– About This Skill

name: plagiarism-checker-pre-screener description: "Use when: User provides text/document and asks to check originality,\ \ \ndetect plagiarism, assess similarity, or rewrite high-duplicate content.\nTriggers:\ \ \"check plagiarism\", \"originality check\", \"similarity detection\",\n\"ๆ”นๅ†™้‡ๅคๅ†…ๅฎน\"\ , \"้™้‡\", \"ๆŸฅ้‡\", \"ๅŽŸๅˆ›ๆ€งๆฃ€ๆต‹\", \"ๆŠ„่ขญๆฃ€ๆŸฅ\"\nInput: Text content or document (txt, md,\ \ docx support via text extraction)\nOutput: Originality score, highlighted duplicate/similar\ \ paragraphs, paraphrasing suggestions" version: 1.0.0 category: Research tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

Plagiarism Checker Pre-Screener

Pre-screens text for potential plagiarism by detecting similarity patterns and providing paraphrasing suggestions for high-duplicate sections.

Technical Difficulty: High โš ๏ธ

> AI่‡ชไธป้ชŒๆ”ถ็Šถๆ€: ้œ€ไบบๅทฅๆฃ€ๆŸฅ > This skill uses advanced NLP techniques. Results should be manually reviewed before submission.

Features

1. Text Similarity Detection: Identifies potentially plagiarized or highly similar text segments 2. Originality Scoring: Provides overall originality percentage (0-100%) 3. Paraphrasing Suggestions: Offers AI-powered rewriting for flagged sections 4. Segment Analysis: Breaks text into sentences/paragraphs for granular checking

Usage

Basic Check

python scripts/main.py --input "Your text here" --threshold 0.75

File Analysis

python scripts/main.py --file document.txt --output report.json

With Paraphrasing

python scripts/main.py --input "text" --paraphrase --style academic

Parameters

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | --input | string | - | Direct text input (alternative to --file) | | --file | path | - | Path to text file to analyze | | --threshold | float | 0.70 | Similarity threshold (0.0-1.0) for flagging | | --paraphrase | flag | false | Enable paraphrasing suggestions | | --style | string | neutral | Paraphrasing style: academic/formal/casual/neutral | | --output | path | stdout | Output file path (JSON format) | | --segments | string | sentence | Analysis unit: sentence/paragraph |

Output Format

{
  "originality_score": 85.5,
  "total_segments": 12,
  "flagged_segments": 2,
  "segments": [
    {
      "index": 1,
      "text": "Original sentence text...",
      "similarity_score": 0.92,
      "flagged": true,
      "paraphrase_suggestion": "Rewritten version..."
    }
  ],
  "summary": "Text shows high originality with minor flagged sections"
}

Implementation Notes

  • Uses TF-IDF + Cosine Similarity for local similarity detection
  • Employs semantic embeddings for meaning-based comparison
  • Paraphrasing uses transformer-based models
  • No external API calls required; runs locally

    • References

  • references/algorithm.md - Technical algorithm details
  • references/paraphrasing_guide.md - Paraphrasing methodology

    • Limitations

    1. Cannot access external databases (internet search required for comprehensive checking) 2. Local similarity only - won't catch plagiarism from external sources 3. Paraphrasing quality depends on input text complexity 4. Processing time increases with document length

    Safety & Privacy

  • All processing is local - no text sent to external APIs
  • Suitable for sensitive/confidential documents
  • No data retention after analysis completes

    • Risk Assessment

    | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited

    • Prerequisites

    # Python dependencies
pip install -r requirements.txt

    Evaluation Criteria

    Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

    • Test Cases

    1. Basic Functionality: Standard input โ†’ Expected output 2. Edge Case: Invalid input โ†’ Graceful error handling 3. Performance: Large dataset โ†’ Acceptable processing time

    Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • - Performance optimization - Additional feature support

    ๐Ÿ’ก Examples

    Basic Check

    python scripts/main.py --input "Your text here" --threshold 0.75

    File Analysis

    python scripts/main.py --file document.txt --output report.json

    With Paraphrasing

    python scripts/main.py --input "text" --paraphrase --style academic

    โš™๏ธ Configuration

    # Python dependencies
pip install -r requirements.txt