sadjad6/outlier_detector
src/skills/data_engineering/outlier_detector/SKILL.md
Detect outliers using statistical methods
tools
Updated Apr 13, 2026
$ install --global
skillsauthnpx skillsauth add sadjad6/open-autonomous-research-lab outlier_detectorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
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3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 13, 2026, 2:32 AM74.0s2 files scanned
SKILL.md
- name:
- outlier_detector
- description:
- Detect outliers using statistical methods
Outlier Detector
Description
Detect outliers using statistical methods
When to Use
When numeric data may contain anomalies
Workflow
- Compute IQR/Z-scores\n2. Flag outliers\n3. Report
Tools Used
pandas, scipy
Examples
from src.skills.data_engineering.outlier_detector import Skill
skill = Skill()
result = await skill.run(SkillInput(data={"key": "value"}))
Best Practices
Use IQR for skewed data, Z-score for normal
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