Agent skill

linkedin-export

Parse, search, analyze, and ingest LinkedIn GDPR data exports into structured JSON or RLAMA for semantic search. Covers messages, connections, profile data, and Markdown export. Requires a LinkedIn GDPR ZIP file. Triggers on 'LinkedIn data', 'search messages', 'analyze connections', 'LinkedIn export', 'GDPR download'.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/linkedin-export

SKILL.md

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 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.

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, reranker enabled, 13 docs, 2.14 MB)

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

bash
# 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 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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results