LinkedIn Intelligence

A Claude skill that turns LinkedIn data exports into an interactive browser dashboard. Analyzes your connections, posts, activity patterns, and network clusters — all locally, no data leaves your machine.

What It Does

Takes a standard LinkedIn data export (ZIP/folder) and generates a single self-contained dashboard.html file with:

Connection Analysis

•Full searchable table of all connections with multi-filter system
•Filter by seniority (C-Level, VP, Director, etc.), company, year, position keywords
•Stack multiple filters, remove them individually
•Seniority auto-classification from job titles
•Names link directly to LinkedIn profiles

Network Visualization

•Top companies and positions in your network (bar charts)
•Seniority distribution (donut chart)
•Network growth over time — monthly and cumulative (line charts)
•Yearly connection breakdown
•Network clusters: companies with 5+ connections (donut + table)
•Dormant connections: people connected 2+ years ago

Post Analysis

•All posts with content preview, type classification, word count, and comment cross-referencing
•Post type distribution: Long Text, Short Text, Media, Link Share, Repost
•Posting patterns: day of week, hour of day
•Word count distribution
•Posts per month timeline
•Filter by type, search by content, link to original post

Activity Patterns

•Monthly activity timeline: posts, comments, reactions
•Reaction type distribution (LIKE, PRAISE, EMPATHY, etc.)
•Reaction activity over time

How to Use

Step 1: Export Your Data from LinkedIn

•Go to linkedin.com/mypreferences/d/download-my-data
•Select all available data (or at minimum: Connections, Shares, Comments, Reactions)
•Click Request archive — LinkedIn emails a download link within 24 hours
•Download the ZIP and extract it (or leave it as a folder)

Step 2: Place the Export

Copy the extracted folder into this project directory. The folder name typically looks like:

code

Complete_LinkedInDataExport_MM-DD-YYYY.zip/

Step 3: Update the Path

Edit build_dashboard.py and set EXPORT_DIR to your folder name:

python

EXPORT_DIR = "./Complete_LinkedInDataExport_01-20-2026.zip"

Step 4: Generate the Dashboard

bash

pip install pandas openpyxl
python build_dashboard.py

Step 5: Open

bash

open dashboard.html   # macOS
xdg-open dashboard.html  # Linux
start dashboard.html  # Windows

Required Files from LinkedIn Export

File	Used For
`Connections.csv`	Network analysis, filters, clusters, dormant detection
`Shares.csv`	Post content, type classification, posting patterns
`Comments.csv`	Comment cross-referencing per post
`Reactions.csv`	Reaction types and activity timeline
`Profile.csv`	Name and headline (for dashboard header)

Dashboard Tabs

Tab	Content
Overview	6 stat cards + 4 summary charts
Connections	All connections, searchable with stackable multi-filters
Companies	Top 20 companies bar chart + table
Seniority	Seniority donut + top 20 positions
Growth	Cumulative line chart, monthly bars, yearly breakdown
Posts	Deep post analysis with 5 charts + scrollable feed
Activity	Monthly posts/comments/reactions + reaction type breakdown
Clusters	Companies with 5+ connections
Dormant	Connections from 2+ years ago

Filter System (Connections Tab)

Click + Add Filter to add any combination of:

•Seniority: C-Level / Founder, VP, Director, Head of, Manager / Lead, Senior IC, IC / Specialist, Junior / Associate
•Company: Dropdown of top 100 companies in your network
•Connection Year: Any year present in your data
•Position Contains: Free text search (e.g., "Engineer", "Sales", "Product")

Filters appear as blue tags. Click x on any tag to remove it. Filters stack — results must match ALL active filters.

Technical Details

•Output: Single self-contained HTML file (no server required)
•Charts: Chart.js 4.x loaded from CDN
•Dependencies: Python 3.9+, pandas, openpyxl (for CSV parsing only)
•Performance: Handles 10k+ connections; tables cap at 500 rows with a prompt to filter
•Seniority detection: Rule-based keyword matching on job titles

Data Handling & Privacy

•All processing happens locally — no data is sent anywhere
•The generated dashboard.html contains your connection data embedded as JSON
•Do NOT publish dashboard.html or your LinkedIn export publicly
•.gitignore is preconfigured to exclude all personal data
•LinkedIn CSVs often have 2-3 metadata rows before the header — the script handles this automatically
•Multiline post content in CSVs is handled with on_bad_lines="skip"

Limitations

•No likes/views counts: LinkedIn's data export does not include impression or reaction counts on your own posts. The export only contains the content you posted and your own outbound activity (reactions/comments you gave to others).
•Comment counts are partial: The "comments" shown per post are your own replies cross-referenced by URL, not the total comment count from others.
•No engagement rate: Without impressions data, engagement rate cannot be calculated.
•Position/title parsing: Seniority classification is heuristic-based and may misclassify unusual titles.

Querying Your Data (Natural Language)

Users can ask natural language questions about their LinkedIn data. When a user asks a question about their connections, posts, comments, or reactions, use query_linkedin.py to retrieve the data and answer conversationally.

How to Handle Queries

•Determine what data the user needs
•Run python3 query_linkedin.py with appropriate flags from the project directory
•Parse the JSON output
•Answer the user's question in natural language
•For follow-ups, re-query with different/additional filters

Query Script Reference

bash

# Overview stats
python3 query_linkedin.py --type summary

# Connections — filter by company, seniority, position, year, recency
python3 query_linkedin.py --type connections --company "Google"
python3 query_linkedin.py --type connections --seniority "VP" --seniority "Director"
python3 query_linkedin.py --type connections --position "Engineer"
python3 query_linkedin.py --type connections --year 2025
python3 query_linkedin.py --type connections --recent 90
python3 query_linkedin.py --type connections --search "John"

# Posts — filter by search term, type, year
python3 query_linkedin.py --type posts --search "RevOps"
python3 query_linkedin.py --type posts --post-type "Long Text"
python3 query_linkedin.py --type posts --year 2025

# Comments — filter by search, year
python3 query_linkedin.py --type comments --search "congratulations"

# Reactions — distribution by type
python3 query_linkedin.py --type reactions --year 2025

# Messages — conversations with search, recency, and follow-up detection
python3 query_linkedin.py --type messages --recent 30
python3 query_linkedin.py --type messages --awaiting-reply
python3 query_linkedin.py --type messages --search "Tim"
python3 query_linkedin.py --type messages --recent 30 --awaiting-reply

# Control output size
python3 query_linkedin.py --type connections --company "Netguru" --limit 20

Flags

Flag	Description
`--type`	`summary`, `connections`, `posts`, `comments`, `reactions`, `messages`
`--company`	Filter connections by company (partial match, case-insensitive)
`--seniority`	Filter by seniority level (can repeat for multiple levels)
`--position`	Filter by position keyword (partial match)
`--search`	Full-text search across relevant fields
`--year`	Filter by year
`--recent N`	Connections/messages from last N days
`--post-type`	Filter posts: Long Text, Short Text, Media, Link Share, Repost
`--awaiting-reply`	Messages only: show conversations where the other person messaged last
`--limit N`	Max results (default 500)

Example Conversations

User: "Who do I know at Google?" → Run: python3 query_linkedin.py --type connections --company "Google" → Present the list with names, positions, and connection dates

User: "Show me VP and Director level connections" → Run: python3 query_linkedin.py --type connections --seniority "VP" --seniority "Director" → Summarize by company, list the connections

User: "Summarize my posts about RevOps" → Run: python3 query_linkedin.py --type posts --search "RevOps" → Read the post content, generate a thematic summary

User: "How many people did I connect with last month?" → Run: python3 query_linkedin.py --type connections --recent 30 → Count and describe the results

User: "What are the top companies in my network?" → Run: python3 query_linkedin.py --type summary → Present the top_companies from the summary

User: "Find all founders I'm connected with" → Run: python3 query_linkedin.py --type connections --seniority "C-Level / Founder" → List or summarize the results

User: "Summarize my recent LinkedIn conversations" → Run: python3 query_linkedin.py --type messages --recent 30 → Summarize conversations, highlight which need follow-up

User: "Do I need to reply to anyone?" → Run: python3 query_linkedin.py --type messages --recent 30 --awaiting-reply → Show conversations where the other person messaged last, categorize by priority (genuine vs spam/cold outreach)

User: "What did I discuss with Tim?" → Run: python3 query_linkedin.py --type messages --search "Tim" → Show matching conversations

Tips for Query Handling

•Combine multiple filters for precise queries (e.g., --seniority "VP" --company "Stripe")
•Use --search for fuzzy matching across name, company, and position fields
•For post content analysis, use --type posts --search "topic" and then reason over the full content in results
•When the user asks about trends or patterns, query the raw data and compute the analysis yourself
•The summary endpoint is lightweight — use it for overview questions without pulling full records

Common Issues

Issue	Fix
`ParserError` on CSV	Script uses `on_bad_lines="skip"` — some multiline posts may be skipped
Dates not parsing	Uses `format="mixed"` with `dayfirst=True` for LinkedIn's format
Empty company/position	Filled with "Not Specified" automatically
Charts don't render	Open in Chrome/Firefox/Edge — Safari may lag on large datasets
Too many connections to display	Use filters to narrow down — table shows max 500 rows