LinkedIn Export Skill

Parse LinkedIn GDPR data exports into structured JSON, then search messages, analyze connections, export to Markdown, and ingest into RLAMA for semantic search.

Prerequisites

• Python 3.10+ via uv

• LinkedIn GDPR export ZIP — Request at: LinkedIn → Settings → Data Privacy → Get a copy of your data

• RLAMA + Ollama (optional, for semantic search ingestion)

Quick Start

1. Parse the export ZIP (run once)

uv run ~/.claude/skills/linkedin-export/scripts/li_parse.py ~/Downloads/Basic_LinkedInDataExport_*.zip

2. Search, analyze, export, or ingest

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --list-partners uv run ~/.claude/skills/linkedin-export/scripts/li_network.py summary uv run ~/.claude/skills/linkedin-export/scripts/li_export.py all --output ~/linkedin-archive/ uv run ~/.claude/skills/linkedin-export/scripts/li_ingest.py

All scripts read from ~/.claude/skills/linkedin-export/data/parsed.json . Parse once, query many times.

Parse — li_parse.py

Unzip and parse all CSVs from the LinkedIn GDPR export into structured JSON.

uv run ~/.claude/skills/linkedin-export/scripts/li_parse.py <linkedin-export.zip> uv run ~/.claude/skills/linkedin-export/scripts/li_parse.py <zip> --output /custom/path.json

Output: ~/.claude/skills/linkedin-export/data/parsed.json

Parses: messages, connections, profile, positions, education, skills, endorsements, invitations, recommendations, shares, reactions, certifications.

Auto-detects CSV column names (case-insensitive) to handle LinkedIn format changes between exports.

Search Messages — li_search.py

Search messages by person, keyword, date range, or combination.

Search by person

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --person "Jane Doe"

Search by keyword

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --keyword "project proposal"

Date range

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --after 2025-01-01 --before 2025-06-01

Combined filters

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --person "Jane" --keyword "meeting" --after 2025-06-01

Full conversation by ID

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --conversation "CONVERSATION_ID"

List all conversation partners (sorted by message count)

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --list-partners

Show context around matches

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --keyword "AI" --context 3

Full message content + JSON output

uv run ~/.claude/skills/linkedin-export/scripts/li_search.py --keyword "proposal" --full --json

Flags: --person , --keyword , --after , --before , --conversation , --list-partners , --context N , --full , --limit N , --json

Network Analysis — li_network.py

Analyze the connection graph — companies, roles, timeline.

Summary stats

uv run ~/.claude/skills/linkedin-export/scripts/li_network.py summary

Top companies by connection count

uv run ~/.claude/skills/linkedin-export/scripts/li_network.py companies --top 20

Connection timeline

uv run ~/.claude/skills/linkedin-export/scripts/li_network.py timeline --by year uv run ~/.claude/skills/linkedin-export/scripts/li_network.py timeline --by month

Role/title distribution

uv run ~/.claude/skills/linkedin-export/scripts/li_network.py roles --top 20

Search connections

uv run ~/.claude/skills/linkedin-export/scripts/li_network.py search "Anthropic"

Export connections to CSV or JSON

uv run ~/.claude/skills/linkedin-export/scripts/li_network.py export --format csv uv run ~/.claude/skills/linkedin-export/scripts/li_network.py export --format json

Subcommands: summary , companies , timeline , roles , search , export

Export to Markdown — li_export.py

Convert parsed data to clean Markdown files.

Export messages (one file per conversation)

uv run ~/.claude/skills/linkedin-export/scripts/li_export.py messages --output ~/linkedin-archive/messages/

Export connections as Markdown table

uv run ~/.claude/skills/linkedin-export/scripts/li_export.py connections --output ~/linkedin-archive/connections.md

Export everything

uv run ~/.claude/skills/linkedin-export/scripts/li_export.py all --output ~/linkedin-archive/

Export RLAMA-optimized documents

uv run ~/.claude/skills/linkedin-export/scripts/li_export.py rlama --output ~/linkedin-archive/rlama/

Subcommands: messages , connections , all , rlama

RLAMA Ingestion — li_ingest.py

Prepare RLAMA-optimized documents and create a semantic search collection.

Full pipeline: prepare docs + create RLAMA collection

uv run ~/.claude/skills/linkedin-export/scripts/li_ingest.py

Prepare docs only (no RLAMA required)

uv run ~/.claude/skills/linkedin-export/scripts/li_ingest.py --prepare-only

Rebuild existing collection

uv run ~/.claude/skills/linkedin-export/scripts/li_ingest.py --rebuild

Collection: linkedin-tdimino (fixed/600/100 chunking, BM25-heavy hybrid search)

Query examples:

rlama run linkedin-tdimino --query "What did I discuss with [person]?" rlama run linkedin-tdimino --query "Who works at [company]?" rlama run linkedin-tdimino --query "What are my top skills?"

RLAMA document structure:

• messages-conversations-{a-f,g-l,m-r,s-z}.md — Conversations grouped alphabetically

• connections-companies.md — Connections by company

• connections-timeline.md — Connections by year

• profile-positions-education.md — Resume data

• endorsements-skills.md — Skills and endorsements

• shares-reactions.md — Posts and activity

• INDEX.md — Collection metadata

Data Format Reference

See references/linkedin-export-format.md for complete CSV column documentation.

Key files in the LinkedIn export ZIP:

CSV Contents

messages.csv

All messages and InMail

Connections.csv

1st-degree connections

Profile.csv

Profile data

Positions.csv

Work history

Education.csv

Education

Skills.csv

Listed skills

Endorsement_Received_Info.csv

Endorsements

Invitations.csv

Connection requests

Recommendations_Received.csv

Recommendations

Shares.csv

Posts and shares

Reactions.csv

Post reactions

Certifications.csv

Certifications

Script Selection Guide

Task Script Example

First-time setup li_parse.py

Parse the ZIP

Find a conversation li_search.py --person

Search by person name

Find a topic li_search.py --keyword

Search by keyword

Who do I talk to most? li_search.py --list-partners

Sorted partner list

Company breakdown li_network.py companies

Top companies

Network growth li_network.py timeline

Connections over time

Archive messages li_export.py messages

Markdown per conversation

Semantic search li_ingest.py

RLAMA collection