Skip to main content

md2linkedin

PyPI version Python versions PyPI Downloads

md2linkedin converts Markdown text to LinkedIn-compatible plain text by replacing bold, italic, and bold-italic markers with Unicode Mathematical Sans-Serif characters. This preserves visual formatting when pasting into platforms like LinkedIn that do not support Markdown natively.

Installation

Package Manager Installation Command
pip pip install md2linkedin
uv uv add md2linkedin

Tip: Run the CLI without installing it with uvx md2linkedin post.md.

Usage

Python API

# @pyodide
from md2linkedin import convert

md = """
# Exciting News

I'm thrilled to share that **we just launched** a new product!

Key highlights:

- **Performance**: *3x faster* than the previous version
- **Reliability**: ***zero downtime*** deployments
- **Developer UX**: clean, intuitive API

Check it out and let me know what you think.
"""

print(convert(md))
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
𝗘𝗫𝗖𝗜𝗧𝗜𝗡𝗚 𝗡𝗘𝗪𝗦
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

I'm thrilled to share that 𝘄𝗲 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 a new product!

Key highlights:

• 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: 3𝘹 𝘧𝘢𝘴𝘵𝘦𝘳 than the previous version
• 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝙯𝙚𝙧𝙤 𝙙𝙤𝙬𝙣𝙩𝙞𝙢𝙚 deployments
• 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗨𝗫: clean, intuitive API

Check it out and let me know what you think.

CLI

# Convert a Markdown file (output: post.linkedin.txt)
md2linkedin post.md

# Specify output path
md2linkedin post.md -o linkedin_post.txt

# Pipe from stdin
echo "**Hello**, *world*!" | md2linkedin

# Keep link URLs in the output
md2linkedin post.md --preserve-links

# Disable monospace code rendering
md2linkedin post.md --no-monospace-code

Key Features

  • Bold: **text** or __text__ → Unicode Sans-Serif Bold (𝗯𝗼𝗹𝗱)
  • Italic: *text* or _text_ → Unicode Sans-Serif Italic (𝘪𝘵𝘢𝘭𝘪𝘤)
  • Bold-italic: ***text*** or ___text___ → Unicode Sans-Serif Bold Italic (𝙗𝙤𝙡𝙙-𝙞𝙩𝙖𝙡𝙞𝙘)
  • Headers: #/##/etc. styled with bold Unicode; H1 gets a border
  • Code spans: backticks stripped, content rendered in Unicode Monospace (𝚌𝚘𝚍𝚎) by default; --no-monospace-code keeps plain text
  • Fenced code blocks: fences stripped and content rendered in Unicode Monospace by default; --no-monospace-code preserves verbatim
  • Links: stripped to display text by default; --preserve-links retains URLs
  • Images: replaced by alt text
  • Bullet lists: -/*/+; nested items get one marker per level (, , )
  • Blockquotes: leading > stripped
  • HTML spans: unwrapped, inner text preserved
  • HTML entities: decoded (&&, etc.)
  • Backslash escapes: resolved (\**)
  • Windows line endings: normalised automatically
  • Emojis & non-ASCII: pass through unchanged — no accidental corruption

Limitations

Converting Markdown to LinkedIn-friendly text relies on Unicode Mathematical Alphanumeric Symbols to simulate styling. This approach has notable limitations:

  • Code Blocks: Monospace Unicode characters do not enforce true fixed-width alignment on proportional fonts (like LinkedIn’s default font). As a result, indentation and column alignment in code blocks will often break visually.
  • Tables: Markdown tables are not converted — they pass through as raw pipe syntax (| col | col |), which LinkedIn does not render, producing unreadable output.
  • Accessibility: Screen readers often read Unicode mathematical characters aloud individually (e.g., “mathematical sans-serif bold b”) instead of as complete words, making the content difficult for visually impaired users to understand.
  • Searchability: Text styled with these Unicode characters may not be indexed properly by LinkedIn’s search algorithm, meaning people searching for your keywords might not find your post.
  • Markdown parsing: The converter is a pipeline of regular expressions, not a CommonMark parser. Ordinary posts convert faithfully, but constructs whose meaning depends on their surroundings — indented code blocks, nested blockquotes, link reference definitions, a thematic break indented inside a list item — are read more loosely than a real parser would read them. See Markdown parsing fidelity for the specifics.

For more examples, check out the package documentation at: https://www.indrapatil.com/md2linkedin/

See Also

License

This project is licensed under the MIT License.

Code of Conduct

Please note that the md2linkedin project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Acknowledgements

Hex sticker font is Rubik. Icons are sourced from Flaticon:

Release files for md2linkedin 0.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for md2linkedin 0.4.0
File Size Uploaded
md2linkedin-0.4.0.tar.gz 20.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for md2linkedin 0.4.0
File Interpreter ABI Platform
md2linkedin-0.4.0-py3-none-any.whl Python 3 none any Details

Total release size: 41.8 kB

Release files / md2linkedin-0.4.0.tar.gz

Download URL md2linkedin-0.4.0.tar.gz
Size 20.9 kB
Tags Source
SHA-256 checksum
How to use checksums
2510d714cf60283b89d5426e5fd5255f8a54f84cf78e60daabf47c6c6e99e5ca
BLAKE2b-256 checksum
How to use checksums
2ed7f5a478ac681263186a03979c8781f27be5766b7bf0b0131e2b4f466cafe2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 20, 2026.

Transparency log

Release files / md2linkedin-0.4.0-py3-none-any.whl

Download URL md2linkedin-0.4.0-py3-none-any.whl
Size 20.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7645caae5923fbb519b930ce5d83955e533837eb9a9ac311003a03453358e75e
BLAKE2b-256 checksum
How to use checksums
049b47e134ff43cb85842461ed82678f796de6843d06069474308740074929fc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 20, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.4.0 This release

2 release files

0.3.0

2 release files

0.2.3

2 release files

0.2.2

2 release files

0.2.1

2 release files

0.2.0

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page