linkedin2md 0.3.1

Convert LinkedIn data exports to Markdown

linkedin2md

Convert LinkedIn data exports to clean Markdown files - the ideal format for LLM analysis.

Why Markdown?

Markdown is the lingua franca of AI tools. Once your LinkedIn data is in .md format, you can:

• Upload to NotebookLM and have conversations about your career history
• Use Claude Projects to analyze patterns across your professional journey
• Feed to Obsidian with AI plugins for a personal career knowledge base
• Run local LLMs (Ollama, LM Studio) for completely private analysis

Example Prompts

Once your LinkedIn data is in an LLM, try asking:

Question	Data Used
"What patterns do you see in my career transitions?"	experience.md
"What skills have I developed over time?"	skills.md, experience.md
"Group my connections by industry"	connections.md
"What themes appear in my recommendations?"	recommendations.md
"Summarize my job applications and outcomes"	job_applications.md
"What qualities do people consistently mention about me?"	recommendations.md, endorsements.md
"Based on my experience, what roles should I target?"	All files

Installation

Recommended (using pipx - installs in isolated environment):

pipx install linkedin2md

Or with pip (in a virtual environment):

pip install linkedin2md

Note: On modern Linux systems (Debian, Ubuntu 23.04+, Fedora), use pipx to avoid the "externally-managed-environment" error.

Usage

linkedin2md Complete_LinkedInDataExport.zip
linkedin2md export.zip -o ./my-profile
linkedin2md export.zip --lang es
linkedin2md export.zip --pdf  # Generate beautiful PDF CV alongside Markdown files

Then drag the output folder into your favorite AI tool.

Options

Option	Description	Default
source	LinkedIn ZIP export file (required)	-
-o, --output	Output directory	linkedin_export
--lang	Output language (en or es)	en
--pdf	Generate an elegant A4 PDF resume from your profile	False

How to Export Your LinkedIn Data

Follow these steps to download your LinkedIn data:

1. Go to Settings: Log into LinkedIn, click your profile photo in the top right, then select "Settings & Privacy"

2. Navigate to Data Privacy: In the left sidebar, click "Data privacy"

3. Request your data: Click "Get a copy of your data"

4. Select data to download:

   • Choose "Download larger data archive" for complete data
   • Or select specific categories if you only need certain data

5. Request archive: Click "Request archive"

6. Wait for email: LinkedIn will process your request and send an email when ready (usually within 24 hours, sometimes up to 72 hours for large archives)

7. Download the ZIP file: Click the download link in the email. The file will be named something like Complete_LinkedInDataExport_01-20-2025.zip

Note: The download link expires after a few days. Download it promptly and store it safely.

LLM Tools That Work Great With This

Tool	Type	Best For
NotebookLM	Cloud	Conversational analysis, audio summaries
Claude Projects	Cloud	Deep analysis, long context
ChatGPT	Cloud	General analysis, quick insights
Obsidian + AI plugins	Local	Personal knowledge base, linked notes
Open Notebook	Local/Cloud	16+ AI models, open source
Ollama	Local	Private, offline analysis

Output

Creates 40+ markdown files in the output directory, organized by category:

Core Profile

• profile.md - Name, title, contact, summary
• experience.md - Work history with achievements
• education.md - Educational background
• skills.md - Professional skills
• certifications.md - Certifications and licenses
• languages.md - Language proficiencies
• projects.md - Personal and professional projects

Recommendations & Endorsements

• recommendations.md - Recommendations received
• endorsements.md - Skill endorsements received

Learning

• learning.md - LinkedIn Learning courses
• learning_reviews.md - Course reviews and ratings

Network

• connections.md - Your connections
• companies_followed.md - Companies you follow
• members_followed.md - People you follow
• invitations.md - Connection invitations sent/received
• imported_contacts.md - Contacts imported from address book

Content & Activity

• posts.md - Your posts and shares
• comments.md - Comments you made
• reactions.md - Likes and reactions
• reposts.md - Content you reposted
• votes.md - Poll votes
• saved_items.md - Bookmarked content
• events.md - Events attended

Job Search

• job_applications.md - All job applications
• saved_jobs.md - Jobs you saved
• job_preferences.md - Job seeker preferences
• saved_job_answers.md - Saved application answers
• screening_responses.md - Screening question responses
• saved_job_alerts.md - Job alert settings

Activity History

• search_queries.md - Search history
• logins.md - Login history
• security_challenges.md - Security verification events

Advertising & Privacy

• ads_clicked.md - Ads you clicked
• ad_targeting.md - How LinkedIn targets ads to you
• lan_ads.md - LinkedIn Audience Network engagement
• inferences.md - LinkedIn's inferences about you

Payments

• receipts.md - Premium subscription receipts

Services Marketplace

• service_engagements.md - Service provider engagements
• service_opportunities.md - Service opportunities

Identity

• verifications.md - Identity verifications
• identity_assets.md - Uploaded documents (resumes, etc.)

📄 PDF Resume Generation

With the --pdf flag, linkedin2md converts your parsed structured data into an elegant, print-ready, professional A4 PDF Resume (profile.pdf):

• Design: Hand-crafted executive layout with pristine typography, clean margins, and print-friendly stylesheets.
• Prerequisites: Requires weasyprint and markdown libraries to be installed locally:

  pip install weasyprint markdown
  

  (If not installed, linkedin2md will continue to generate your Markdown folder safely, omitting the PDF and providing setup instructions).

🤖 Agentic Development (NERV Framework)

This repository is fully configured for Agentic Development using opencode and the NERV orchestration framework. NERV coordinates specialized autonomous subagents to design, program, test, and audit code changes.

Quick Commands (Inside Opencode/Claude Code TUI):

• SDD Pipeline (/sdd-new <change>): Starts the 8-phase Spec-Driven Development workflow (explore ➔ propose ➔ spec ➔ design ➔ tasks ➔ apply ➔ verify ➔ archive) utilizing custom subagents in .opencode/agents/ and .opencode/skills/.
• Adversarial Review (/judgment-day): Triggers a dual-model adversarial code review (e.g. Claude vs. Copilot) to stress-test pull requests against AGENTS.md standards.
• Standards Audit (/review): Automatically audits files against strict linting and universal code standards.

We welcome PRs developed and verified using your own AI agent setups!

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

GPL-2.0 - see LICENSE for details.