markdown

name	markdown
description	Extract text and tables from Markdown (.md) files. Use when a user uploads a Markdown document that needs to be processed.

Markdown Processing for Text Extraction

Purpose

Extract readable text and structured tables from Markdown files. This skill feeds extracted content into downstream processing such as document summarization for PPT generation.

When to Use

• User uploads a .md file
• Need to extract document content for summarization, analysis, or conversion to other formats
• Markdown contains code blocks, tables, or structured content that should be preserved

Approach

Step 1: Read the File Directly

Markdown is plain text, so read it directly.

with open("document.md", "r", encoding="utf-8") as f:
    raw_text = f.read().strip()

If UTF-8 fails, try utf-8-sig or gbk.

Step 2: Normalize and Strip Frontmatter

def strip_frontmatter(text: str) -> str:
    if text.startswith("---"):
        end = text.find("\n---", 3)
        if end != -1:
            return text[end + 4 :].lstrip()
    return text

text = raw_text.replace("\r\n", "\n").replace("\r", "\n")
text = strip_frontmatter(text)

Step 3: Extract Tables When Present

import re

def extract_markdown_tables(text: str) -> list[list[list[str]]]:
    tables: list[list[list[str]]] = []
    current_table: list[list[str]] = []

    for line in text.split("\n"):
        line = line.strip()
        if not line.startswith("|"):
            if current_table:
                tables.append(current_table)
                current_table = []
            continue

        if re.match(r"^\|[\s\-:|]+\|$", line):
            continue

        cells = [cell.strip() for cell in line.strip("|").split("|")]
        if cells:
            current_table.append(cells)

    if current_table:
        tables.append(current_table)
    return tables

Handling Common Issues

Problem	Solution
UTF-8 decode fails	Retry with utf-8-sig or gbk
YAML frontmatter pollutes content	Strip the opening --- ... --- block
Large file	Truncate or summarize in chunks before downstream use
No tables present	Return an empty list

Integration Notes

For the upload-to-PPT workflow:

1. Read raw Markdown text
2. Extract tables if present
3. Pass the result to document-summarizer
4. Then call generate_ppt(content=...)

Output Format

Return (raw_text: str, tables: list[list[list[str]]], estimated_pages: int):

• raw_text: normalized markdown text
• tables: extracted markdown tables as list[list[str]]
• estimated_pages: rough estimate, e.g. max(1, len(text) // 3000)