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

bash

# 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.

bash

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.

bash

# 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.

bash

# 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.

bash

# 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.

bash

# 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:

bash

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

linkedin-export

NPX Install

Tags

SKILL.md Content

LinkedIn Export Skill

Prerequisites

Quick Start

Parse —
`li_parse.py`

Search Messages —
`li_search.py`

Network Analysis —
`li_network.py`

Export to Markdown —
`li_export.py`

RLAMA Ingestion —
`li_ingest.py`

Data Format Reference

Script Selection Guide