Skill: aipoch-ai/outlier-detection-handler (version 1.0.1)

--- name: outlier-detection-handler description: Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries. license: MIT skill-author: AIPOCH --- # Outlier Detection & Handling Identify and manage statistical outliers. ## When to Use - Use this skill when the task needs Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries. - Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format. - Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence. ## Key Features - Scope-focused workflow aligned to: Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries. - Packaged executable path(s): `scripts/main.py`. - Structured execution path designed to keep outputs consistent and reviewable. ## Dependencies See `## Prerequisites` above for related details. - `Python`: `3.10+`. Repository baseline for current packaged skills. - `numpy`: `unspecified`. Declared in `requirements.txt`. - `scipy`: `unspecified`. Declared in `requirements.txt`. ## Example Usage ```bash cd "20260318/scientific-skills/Data Analytics/outlier-detection-handler" python -m py_compile scripts/main.py python scripts/main.py --help ``` Example run plan: 1. Confirm the user input, output path, and any required config values. 2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings. 3. Run `python scripts/main.py` with the validated inputs. 4. Review the generated output and return the final artifact with any assumptions called out. ## Implementation Details See `## Workflow` above for related details. - Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable. - Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script. - Primary implementation surface: `scripts/main.py`. - Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints. - Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects. ## Quick Check Use this command to verify that the packaged script entry point can be parsed before deeper execution. ```bash python -m py_compile scripts/main.py ``` ## Audit-Ready Commands Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths. ```bash python -m py_compile scripts/main.py python scripts/main.py --help ``` ## Workflow 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions. 3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available. 4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items. 5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion. ## 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 ```text # 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 ## Output Requirements Every final response should make these items explicit when they are relevant: - Objective or requested deliverable - Inputs used and assumptions introduced - Workflow or decision path - Core result, recommendation, or artifact - Constraints, risks, caveats, or validation needs - Unresolved items and next-step checks ## Error Handling - If required inputs are missing, state exactly which fields are missing and request only the minimum additional information. - If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment. - If `scripts/main.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback. - Do not fabricate files, citations, data, search results, or execution outcomes. ## Input Validation This skill accepts requests that match the documented purpose of `outlier-detection-handler` and include enough context to complete the workflow safely. Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond: > `outlier-detection-handler` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill. ## Response Template Use the following fixed structure for non-trivial requests: 1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits 7. Next Checks If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

path: requirements.txt
content: numpy
scipy


path: scripts/main.py
content: #!/usr/bin/env python3
"""
Outlier Detection Handler
Statistical outlier identification and handling recommendations.
"""

import argparse
import numpy as np
from scipy import stats


class OutlierDetector:
    """Detect and handle statistical outliers."""
    
    def zscore_method(self, data, threshold=3):
        """Detect outliers using Z-score."""
        z_scores = np.abs(stats.zscore(data))
        outliers = np.where(z_scores > threshold)[0]
        return outliers, z_scores
    
    def iqr_method(self, data):
        """Detect outliers using IQR."""
        Q1 = np.percentile(data, 25)
        Q3 = np.percentile(data, 75)
        IQR = Q3 - Q1
        
        lower_bound = Q1 - 1.5 * IQR
        upper_bound = Q3 + 1.5 * IQR
        
        outliers = np.where((data < lower_bound) | (data > upper_bound))[0]
        return outliers, (lower_bound, upper_bound)
    
    def grubbs_test(self, data, alpha=0.05):
        """Grubbs' test for outliers."""
        n = len(data)
        mean = np.mean(data)
        std = np.std(data, ddof=1)
        
        G = np.max(np.abs(data - mean)) / std
        
        # Critical value (simplified)
        t_critical = stats.t.ppf(1 - alpha / (2 * n), n - 2)
        G_critical = ((n - 1) / np.sqrt(n)) * np.sqrt(t_critical**2 / (n - 2 + t_critical**2))
        
        is_outlier = G > G_critical
        
        return is_outlier, G, G_critical
    
    def recommend_handling(self, outlier_count, total_count):
        """Recommend outlier handling approach."""
        percentage = (outlier_count / total_count) * 100
        
        if percentage < 1:
            return "Remove outliers - likely data entry errors"
        elif percentage < 5:
            return "Investigate outliers - may be legitimate extreme values"
        else:
            return "Consider robust statistical methods - high outlier percentage"


def main():
    parser = argparse.ArgumentParser(description="Outlier Detection Handler")
    parser.add_argument("--data", "-d", help="Data file (one value per line)")
    parser.add_argument("--method", "-m", choices=["zscore", "iqr", "grubbs"],
                       default="iqr", help="Detection method")
    parser.add_argument("--threshold", "-t", type=float, default=3,
                       help="Z-score threshold")
    
    args = parser.parse_args()
    
    detector = OutlierDetector()
    
    if args.data:
        with open(args.data) as f:
            data = np.array([float(line.strip()) for line in f if line.strip()])
    else:
        # Demo data
        data = np.array([10, 12, 11, 13, 12, 11, 100, 12, 11, 13])
    
    print(f"\n{'='*60}")
    print("OUTLIER DETECTION RESULTS")
    print(f"{'='*60}\n")
    
    print(f"Data points: {len(data)}")
    print(f"Mean: {np.mean(data):.2f}")
    print(f"Std: {np.std(data):.2f}")
    print()
    
    if args.method == "zscore":
        outliers, scores = detector.zscore_method(data, args.threshold)
        print(f"Method: Z-score (threshold = {args.threshold})")
        
    elif args.method == "iqr":
        outliers, bounds = detector.iqr_method(data)
        print(f"Method: IQR")
        print(f"Bounds: [{bounds[0]:.2f}, {bounds[1]:.2f}]")
        
    elif args.method == "grubbs":
        is_outlier, G, G_critical = detector.grubbs_test(data)
        print(f"Method: Grubbs' test")
        print(f"G statistic: {G:.3f}")
        print(f"G critical: {G_critical:.3f}")
        outliers = [np.argmax(np.abs(data - np.mean(data)))] if is_outlier else []
    
    print(f"\nOutliers detected: {len(outliers)}")
    if len(outliers) > 0:
        print("Outlier values:")
        for idx in outliers:
            print(f"  Index {idx}: {data[idx]}")
    
    recommendation = detector.recommend_handling(len(outliers), len(data))
    print(f"\nRecommendation: {recommendation}")
    
    print(f"\n{'='*60}\n")


if __name__ == "__main__":
    main()


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)