linkedin-export

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 23 CSV types:

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

Extended: comments (548), projects (3), honors (2), organizations (3), volunteering (1), languages (9), events (12), member_follows (828), job_applications (443, merged from multiple files), recommendations_given (3), inferences (4)

Auto-detects CSV column names (case-insensitive), handles LinkedIn's preamble format (Connections.csv), and merges split files (Job Applications).

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, reranker enabled, 13 docs, 2.14 MB)

Query (default: retrieve-only, Claude synthesizes):

# Retrieve raw chunks — Claude reads and synthesizes (best quality)
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py linkedin-tdimino "What projects has Tom built?" -k 10

# Fallback: local LLM answers (only without Claude)
rlama run linkedin-tdimino --query "Who works at Google?"

RLAMA document structure (13 files):

• 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
• comments-activity.md — 548 comments with dates and links
• projects-honors-volunteering.md — Projects, honors, volunteering, organizations
• metadata-languages-events-follows.md — Languages, events, follows, job applications, recommendations given, inferences
• INDEX.md — Collection metadata and counts

Data Format Reference

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

Key files in the LinkedIn export ZIP (23 parsed):

| CSV | Contents | |-----|----------| | messages.csv | All messages and InMail | | Connections.csv | 1st-degree connections (preamble format) | | Profile.csv | Profile data | | Positions.csv | Work history | | Education.csv | Education | | Skills.csv | Listed skills | | Endorsement_Received_Info.csv | Endorsements received | | Invitations.csv | Connection requests | | Recommendations_Received.csv | Recommendations received | | Shares.csv | Posts and shares | | Reactions.csv | Post reactions | | Certifications.csv | Certifications | | Comments.csv | Comments on posts | | Projects.csv | Projects (Bazaar, Dream Daimon, etc.) | | Honors.csv | Awards and hackathon wins | | Organizations.csv | Clubs and groups | | Volunteering.csv | Volunteer roles | | Languages.csv | Language proficiencies | | Events.csv | LinkedIn events | | Member_Follows.csv | People/companies followed | | Jobs/Job Applications*.csv | Job applications (split across multiple files) | | Recommendations_Given.csv | Recommendations written | | Inferences_about_you.csv | LinkedIn's inferences |

Script Selection Guide

| Task | Script | Example | |------|--------|---------| | First-time setup | li_parse.py | Parse the ZIP | | Find a conversation | li_search.py --person | Se