Skill detail
linkedin-export
Useful LinkedIn GDPR export analysis, but only for exported personal data.
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SKILL.md
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--- name: linkedin-export description: "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'." --- # 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-expRead the full source on GitHub (opens external page)