tomevault-io 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 alphabeticallyconnections-companies.md— Connections by companyconnections-timeline.md— Connections by yearprofile-positions-education.md— Resume dataendorsements-skills.md— Skills and endorsementsshares-reactions.md— Posts and activitycomments-activity.md— 548 comments with dates and linksprojects-honors-volunteering.md— Projects, honors, volunteering, organizationsmetadata-languages-events-follows.md— Languages, events, follows, job applications, recommendations given, inferencesINDEX.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 |
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 |
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