Outlier Detection & Handling

Identify and manage statistical outliers.

Use Cases

• Data quality control
• Pre-analysis screening
• Regulatory compliance (FDA data integrity)

Parameters

Parameter	Type	Required	Default	Description
data	str	Yes	-	Path to dataset file (CSV/Excel)
method	str	No	"3-sigma"	Detection method: "3-sigma", "IQR", or "Grubbs"
action	str	No	"flag"	Handling action: "flag", "remove", or "winsorize"

Returns

• Outlier flagging with method details
• Handling recommendations
• Documentation for regulatory submission

Example

Input: Biomarker measurements from 200 patients Output: 5 outliers identified (2.5%), recommended action: investigate then winsorize

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