When to Use This Skill

/document-extract

Extract and structure content from any document format. Auto-detects the format (PDF, PPTX, DOCX, HTML, plain text) and applies the appropriate extraction method. A general-purpose fallback when you're not sure which specific extraction skill to use.

When to Use This Skill

• Processing a document when you're unsure of the best extraction approach
• Handling formats not covered by specific skills (DOCX, HTML, CSV, etc.)
• Batch-processing a folder of mixed document types
• Extracting content from email attachments or downloaded files

Usage

/document-extract <path-to-file> [--output-type reference|concept|raw]

Parameters

Instructions

Phase 1: Detect Format and Assess

1. Identify file format from the extension:
   • .pdf → Delegate to /pdf-extract approach
   • .pptx → Delegate to /pptx-extract approach
   • .docx → Use python-docx extraction
   • .html / .htm → Parse HTML to Markdown
   • .csv / .xlsx → Extract as tables
   • .txt / .md → Read directly
   • .json / .yaml → Parse and structure
2. Assess document size — For large documents, plan chunked processing
3. Report to user: "Detected , . Extracting as ."

Phase 2: Extract Content

Apply format-appropriate extraction:

For DOCX files:

from docx import Document

def extract_docx(filepath):
    doc = Document(filepath)
    content = []
    for para in doc.paragraphs:
        style = para.style.name
        text = para.text.strip()
        if text:
            content.append({"style": style, "text": text})
    for table in doc.tables:
        rows = []
        for row in table.rows:
            rows.append([cell.text for cell in row.cells])
        content.append({"type": "table", "rows": rows})
    return content

For HTML files:

• Parse with WebFetch or direct HTML-to-Markdown conversion
• Extract main content (ignore navigation, headers, footers)
• Preserve headings, links, tables, and lists

For CSV/XLSX files:

• Read as tabular data
• Identify column headers
• Generate Markdown tables
• Summarise data patterns and key statistics

For JSON/YAML files:

• Parse structure
• Present as formatted code blocks
• Summarise key fields and data patterns

Phase 3: Structure Output

Generate a note based on --output-type:

• reference — Full reference note with frontmatter, summary, and extracted content
• concept — Extract key concepts and create a concept note
• raw — Minimal formatting, just the extracted content

Output Format

---
type: Reference
title: "Reference - <Document Title>"
referenceType: article
created: YYYY-MM-DD
source: "<filename>"
format: "<detected format>"
tags: [content/document, domain/relevant-tag]
summary: "<One-line summary>"
---

# <Document Title>

> **Source:** <filename> | **Format:** <format> | **Extracted:** YYYY-MM-DD

## Summary

<AI-generated summary>

## Key Points

- <Key point 1>
- <Key point 2>

## Content

<Extracted and structured content>

Examples

Example 1: Word Document

/document-extract ~/Documents/requirements-spec.docx

Example 2: CSV Data

/document-extract ~/Downloads/survey-results.csv --output-type raw

Example 3: HTML Page

/document-extract ~/Downloads/saved-page.html --output-type concept

Invoke with: /document-extract <path> to extract content from any supported document format