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.

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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.

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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

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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

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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

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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

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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

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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