Will my Word resume's layout survive conversion?

The visual layout (columns, sidebars, custom fonts) won't — Markdown doesn't do layout. The information is fully preserved and reorganised into a clean linear reading order. The output looks like a hand-written Markdown CV, which is the point.

Are icons (📧, phone icons) preserved?

Unicode icons (📧, 📞, 🔗) are preserved as-is — they're text characters. Image-based icons (PNG icons in the original) are extracted as images and referenced. The Unicode versions render in any Markdown viewer; image versions need the images/ folder shipped alongside.

Do dates and job titles stay structured?

Yes — we recognise typical resume patterns (Job Title · Company · Date Range) and emit them as ### headings so they're scannable in any rendered Markdown view. This also helps AI tools parse the structure.

Can I use this for GitHub Profile README?

Absolutely — that's a primary use case. The Markdown output drops directly into a README.md at the root of your username/username repo and renders on your GitHub profile page.

My resume has tables for skills. Do they stay tables?

Skill tables are converted to GFM tables when they look table-like (clear rows and columns of data). Pure layout tables (one cell per side of the page, used only for alignment) are flattened to lists — the more readable form.

Word Resume to Markdown — CV to GitHub/Obsidian

Why resumes are conversion-hostile

Most modern Word resume templates are built on multi-column layouts (sidebar with skills, main column with experience), with each section in a separate table cell to control alignment. A naive converter dumps the table structure into the Markdown, making the output unreadable. Ours flattens the layout into a logical reading order: name and headline, contact info, summary, experience (reverse chronological), education, skills.

Where to use the result

GitHub profile README (a Markdown CV is increasingly common for developers and technical writers), Obsidian or Logseq vaults for personal knowledge management, static-site generators (Hugo, Jekyll, Eleventy) to publish a personal site, applications that ingest Markdown for AI analysis (some recruiting tools now prefer Markdown over PDF for parseability). If you also have your CV as PDF, see PDF to Markdown.

Word Resume to Markdown — CV Conversion for Portfolios

Why resumes are conversion-hostile

Where to use the result

Before / After

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