--- 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 uv run ~/.claude/skills/linkedin-export/scripts/li_parse.py --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 |