skills.rest
alexhegit
alexhegitCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

system-data-intelligence

Convert local files into structured data with analytical reports and visualizations.

4|Updated Mar 21, 2026
Search Tags:#data visualization#anomaly detection#document parsing#file ingestion#deep data analysis#plotly dashboard#cross-platform automation
One-click install
npx skills add https://github.com/alexhegit/sovereign-IQ --skill system-data-intelligence
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: system-data-intelligence
Source: https://github.com/alexhegit/sovereign-IQ/tree/main/workspace/skills/system-data-intelligence-skill
Command: npx skills add https://github.com/alexhegit/sovereign-IQ --skill system-data-intelligence

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, openpyxl, python-docx, chardet, plotly, matplotlib, kaleido, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill removes the friction of manually opening files, cleaning messy datasets, performing exploratory analytics, and producing professional visual reports by automating end-to-end system file operations plus deep analysis and visualization.

Core Features & Use Cases

  • Cross-platform file intake: Automatically reads spreadsheets, documents, text/markdown, CSV/JSON, and RTZ project files across Windows/macOS/Linux using the best available strategy (COM/offline parsing/conversion).
  • Deep data analysis pipeline: Runs multi-level analysis (descriptive, diagnostic, predictive/time-series when applicable, and prescriptive insights) including data quality checks, distributions, correlations, and anomaly/outlier detection.
  • Professional visualization output: Produces interactive Plotly dashboards and static Matplotlib figures, exporting standardized artifacts for downstream use and auditing.

Use cases include: turning any uploaded Excel/WPS/Word/data file into an analysis_result.json with a summary.md, extracting key tables/text from document-like inputs, and creating dashboards for trends, missing-rate diagnostics, anomalies, and correlation structure.

Quick Start

Tell the agent: "Analyze this file and generate a complete visualization report."

Frequently Asked Questions about system-data-intelligence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate spreadsheet and document parsing with anomaly detection?▼

You can automate cross-platform file ingestion and anomaly detection by having the system parse spreadsheets and documents into structured data, then run multi-level analysis to output JSON reports and Plotly dashboards.

What is the best way to generate a Plotly dashboard from a CSV or Excel file?▼

The best way to generate a Plotly dashboard from a CSV or Excel file is to ingest the spreadsheet into structured data, run diagnostic and predictive analysis, and export interactive Plotly visualizations alongside static Matplotlib figures.

Does the data visualization pipeline work on Windows for Word documents and spreadsheets?▼

Yes, the data visualization pipeline works on Windows, macOS, and Linux. It uses OS-specific parsing strategies like COM to read Word documents and spreadsheets, then performs deep data analysis to generate visual reports.

Can I run predictive time-series analysis and outlier detection on JSON data?▼

Yes, you can run predictive time-series analysis and outlier detection on JSON data. The pipeline converts JSON into structured data, applies statistical models for distributions and correlations, and flags anomalies in the output report.

Why does my document parsing pipeline not working with RTZ project files?▼

Document parsing issues with RTZ project files often occur when the correct OS-specific ingestion strategy is not triggered. The pipeline requires scripted parsing to extract structured data before running deep data analysis and visualization.

Do I need pandas and scipy to generate markdown summaries from local files?▼

Yes, you need pandas and scipy to generate markdown summaries from local files. These dependencies drive the deep data analysis pipeline, handling data quality checks, distributions, and anomaly detection before outputting the final report.

Related Skills

Explore More Data & Analytics Skills→
wuyyyyyouwuyyyyyou

gws-sheets-append

Append rows to Google Sheets from CSV or JSON inputs.

Community
Intermediate
aws-samplesaws-samples
90

file-processing

Process and analyze CSV, JSON, and text data files with automated cleaning and transformation.

Official
Advanced
pluginagentmarketplacepluginagentmarketplace
5

statistics-math

Perform descriptive statistics, hypothesis tests, and regression analysis with Python libraries.

Community
Advanced
alibabaalibaba
10.5k

pdf-extractor

Extract text, tables, and metadata from PDF documents into JSON.

Official
Intermediate