src/datapro/data/skills/document-converter/SKILL.md
Document format conversion tool. Import: PDF/DOCX/PPTX → Markdown (with OCR fallback). Export: Markdown → PDF/DOCX (with cover page, themes). Use for: (1) Converting external documents to Markdown, (2) Generating professional PDF/DOCX from Markdown analysis results.
npx skillsauth add pablodiegoo/data-pro-skill document-converterInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Skill for importing external documents (PDF/DOCX/PPTX) to Markdown and exporting analysis results to professional reports (PDF/DOCX).
Uses markdowner.py with optional OCR fallback.
python3 .agent/skills/document-converter/scripts/markdowner.py input.pdf [--ocr]
Uses compile_report.py for standard reports or Quarto for premium reports.
# Standard PDF
python3 .agent/skills/document-converter/scripts/compile_report.py report.md --format pdf
assets/quarto-templates/ for base structure.sudo apt install poppler-utils tesseract-ocr pandoc texlive-xetex texlive-fonts-extra
pip install pypandoc pdfminer.six pdf2image pytesseract python-pptx Pillow
.agent/skills/document-converter/
├── SKILL.md
├── assets/ # Templates and branding
├── references/ # Report manuals
│ ├── quarto_reports.md
│ └── troubleshooting.md
└── scripts/
├── markdowner.py # Import engine
└── compile_report.py # Export engine
testing
Comprehensive time-series validation and analysis suite. Handles backtesting of trading and non-trading strategies with support for walk-forward validation (training vs testing windows), performance metric calculation (Sharpe, Drawdown, Win Rate), and event-driven resource allocation simulation. Use for: (1) Validating sequential logic on time-series data, (2) Calculating risk-adjusted performance, (3) Simulating constraints in resource distribution, (4) Detecting look-ahead bias through walk-forward testing.
tools
Core statistical analysis and pipeline automation for survey datasets. Use for: (1) Running standard Crosstabs, NPS, Top-Box calculations, (2) Generating complete EDA or Analytics notebooks, (3) Quantitative and qualitative processing of questionnaire data.
development
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testing
Tactical and highly interpretable Machine Learning. Use for: (1) Extracting Feature Importance via Random Forest, (2) Running Permutation Tests, (3) Handling Imbalanced Data (SMOTE).