FRmicrow/data-analyzer
.agents/skills/data-analyzer/SKILL.md
Specialized skill for analyzing, normalizing, and processing football match data (JSON/CSV) and xG (Expected Goals) statistics. Use this when working with data in Scrapbyvideo/, xGData/, or ScreenData/.
development
Updated Apr 16, 2026
$ install --global
skillsauthnpx skillsauth add FRmicrow/dataFootV1 data-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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 16, 2026, 2:00 AM4.5s1 file scanned
SKILL.md
- name:
- data-analyzer
- description:
- Specialized skill for analyzing, normalizing, and processing football match data (JSON/CSV) and xG (Expected Goals) statistics. Use this when working with data in Scrapbyvideo/, xGData/, or ScreenData/.
Data Analyzer Skill
This skill provides expertise in handling the specific data formats used in the statFootV3 project.
Data Domains
- Match Results: Found in
Scrapbyvideo/(structured by league and season). - xG Data: Consolidated JSON files in
xGData/matchXG/. - Raw Screens: OCR data or screenshot extractions in
ScreenData/.
Responsibilities
- Normalization: Ensure team names and league identifiers are consistent across different data sources.
- Validation: Check for missing matches, duplicate entries, or statistical anomalies (e.g., negative xG).
- Transformation: Convert raw extraction results into the consolidated formats required by the database or ML models.
Integration
- Works in tandem with the
project-contextskill to ensure data is correctly ingested into the backend via the established API services. - Supports the
ml-serviceby providing clean, pre-processed training or inference data.
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