Data Pro Max - Data Analysis Intelligence

An AI orchestrator that provides intelligent recommendations for data analysis, visualization, and reporting. It automatically activates for data-intensive tasks and coordinates specialized sub-skills.

1. Integrated Skill Cores

Data Pro Max coordinates these specialized skills:

Core Skill	Functionality	Location
data-manipulation	T-Layer (Preparation, Weights, Map)	📦 data/skills/
data-analysis-suite	All Stats, Causal & Science	📦 data/skills/
geoprocessing-brazil	Geo-spatial & Mapping	📦 data/skills/
data-viz	Statistical Visualization	📦 data/skills/
document-converter	Format Conversion (Import/Export)	📦 data/skills/
duckdb-sql-master	High-performance SQL on local files	📦 data/skills/
time-series-analysis	Validation & metrics for sequence data	📦 data/skills/
clustering-toolkit	Advanced PCA+DBSCAN grouping	📦 data/skills/
context-optimizer	Document decomposition into .agent	📦 data/skills/

Shared Skills (deployed via manifest)

Skill	Purpose	Location
brainstorming	Creative ideation & design	🔗 .agent/skills/ → manifest
document-mastery	Writing quality & Mermaid diagrams	🔗 .agent/skills/ → manifest

Agent-Only Skills (NOT deployed)

Skill	Purpose	Location
skill-creator	Creating and packaging new skills	🏠 .agent/skills/
notebooklm	Querying Google NotebookLM notebooks	🏠 .agent/skills/

2. Master Workflows (Slash Commands)

Command	Workflow	Location
/project-onboarding	Initial setup & rules	📦 Packaged (datapro setup)
/survey-analysis-pipeline	End-to-end execution	📦 Packaged (datapro setup)
/project-harvest	Learning extraction → assets/harvest/	📦 Packaged (datapro setup)
/document-study	Deep analysis of papers/methodology	📦 Packaged (datapro setup)
/notebook-generation	Dual-layered automated notebook reporting	📦 Packaged (datapro setup)
/project-evolution	Absorb harvest into Data-Pro-Skill	🏠 Local (this repo only)

3. High-Performance Workflow

graph TD
    A[User Request] --> B[Data Discovery]
    B --> C{Orchestrator}
    C -->|Transformation| D1[data-manipulation]
    C -->|Statistical| D2[data-analysis-suite]
    D1 --> D2
    C -->|Spatial| E[geoprocessing-brazil]
    C -->|SQL/Large Data| F[duckdb-sql-master]
    D1 & D2 & E & F --> G[data-viz]
    G --> H[document-mastery]
    H --> I[document-converter]
    I --> J[Final Report]

4. Operational Best Practices

Step 1: Integrated Pipeline

Use @data-manipulation for preparation (mapping, cleaning, weighting) and @data-analysis-suite for specialized statistics. Consult the references/*.md inside each skill for specific methodologies.

Step 2: Consistent Aesthetics

Always use data-viz for chart generation to ensure consistent styling and 300 DPI quality.

Step 3: Global Language Policy

All technical artifacts, code comments, and documentation produced MUST be written in English.

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[!IMPORTANT] This repository uses a References Pattern for complex skills. If a task requires specialized stats, read the corresponding file in data-analysis-suite/references/ first.