tdiminotdimino/claude-code-minoan/skills/integration-automation/linkedin-export/SKILL.md

linkedin-export

Skill41 starsChanged 7 months agoMITView on GitHub

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'.

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.

Common
Distinctive
Very specific

Very specific:
All scripts read from ~/.claude/skills/linkedin-export/data/parsed.json.
line 29
Distinctive:
Search messages by person, keyword, date range, or combination.
line 56

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.

Files

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

# 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`	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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