AI Skill Report Card
Detecting File Types
Detecting File Types with AI
Rapidly identify file content types using deep learning models, even when file extensions are missing or incorrect.
Quick Start15 / 15
Bash# Install magika pip install magika # Detect single file magika document.pdf # Scan directory recursively magika -r /path/to/files # Get JSON output with confidence scores magika file.txt --json --output-score
Recommendation▾
Add edge cases like handling corrupted files, empty files, or files that return 'unknown' types
Workflow12 / 15
Basic Detection:
- Install magika CLI or Python package
- Point to file/directory path
- Review detected content type and confidence score
- Use appropriate prediction mode based on accuracy needs
Batch Processing:
Progress:
- [ ] Collect file paths or directory
- [ ] Choose prediction mode (high/medium/best-guess)
- [ ] Run detection with appropriate output format
- [ ] Filter results by confidence threshold
- [ ] Process files based on detected types
Python Integration:
Pythonfrom magika import Magika m = Magika() # Detect from file path result = m.identify_path('document.pdf') print(f"Type: {result.output.label}") print(f"MIME: {result.output.mime_type}") print(f"Confidence: {result.output.score}") # Detect from bytes content = open('file.bin', 'rb').read() result = m.identify_bytes(content) # Detect from stream with open('data.csv', 'rb') as f: result = m.identify_stream(f)
Recommendation▾
Include templates for common integration patterns (web upload validation, file organization scripts, security scanning pipelines)
Examples17 / 20
Example 1:
Input: magika suspicious_file.txt
Output: suspicious_file.txt: Windows PE executable (executable)
Example 2:
Input: magika --json --output-score data.unknown
Output:
JSON{ "path": "data.unknown", "result": { "value": { "output": { "label": "csv", "description": "CSV document", "mime_type": "text/csv", "score": 0.99 } } } }
Example 3:
Input: magika -r ./uploads/ --format "%p: %l (%s%%)"
Output:
./uploads/doc1.pdf: pdf (99%)
./uploads/image.jpg: jpeg (97%)
./uploads/script.py: python (98%)
Recommendation▾
Expand completeness with fallback strategies when Magika fails and alternative tools for specialized file types
Best Practices
- Use prediction modes appropriately:
high-confidencefor security scanning,best-guessfor general classification - Check confidence scores: Scores below 0.8 may need manual review
- Validate critical files: For security applications, combine with additional validation
- Batch process efficiently: Use recursive scanning for directories rather than individual file calls
- Handle generic labels: Files returning "Generic text" or "Unknown binary" may need fallback detection
- Consider file size: Magika analyzes only file headers/beginnings, so works on large files efficiently
Common Pitfalls
- Don't rely solely on extensions: Magika detects actual content, not filename extensions
- Don't ignore confidence thresholds: Low-confidence results may be inaccurate
- Don't process streaming data without buffering: Use
identify_stream()for file handles - Don't assume 100% accuracy: Even with 99% accuracy, validate critical file types
- Don't skip error handling: Check result status before accessing detection values
- Don't use for malware analysis alone: Magika detects file types, not malicious content