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
- Text Similarity Detection: Identifies potentially plagiarized or highly similar text segments
- Originality Scoring: Provides overall originality percentage (0-100%)
- Paraphrasing Suggestions: Offers AI-powered rewriting for flagged sections
- 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 detailsreferences/paraphrasing_guide.md- Paraphrasing methodology
Limitations
- Cannot access external databases (internet search required for comprehensive checking)
- Local similarity only - won't catch plagiarism from external sources
- Paraphrasing quality depends on input text complexity
- 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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