Plagiarism Checker

Use when: User provides text/document and asks to check originality, detect plagiarism, assess similarity, or rewrite high-duplicate content. Triggers: "check plagiarism", "originality check", "similarity detection", "改写重复内容", "降重", "查重", "原创性检测", "抄袭检查" Input: Text content or document (txt, md, docx support via text extraction) Output: Originality score, highlighted duplicate/similar paragraphs, paraphrasing suggestions

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 ⚠️

AI自主验收状态: 需人工检查 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 → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

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