linkedin-intel:profile

linkedin-intel:profile — LinkedIn Profile Intelligence

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Step 0: Load Interpretive Lens

Read ~/.claude/skills/linkedin-intel/SKILL.md into context using the Read tool. Apply its principles (signal hierarchy, extraction rules, topic extraction, confidence calibration, data quality gates) throughout all analysis steps below.

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Step 1: Parse & Setup

1. Extract the LinkedIn profile URL from $ARGUMENTS (strip whitespace).
2. Extract the slug from the URL: split on /in/, take the part after it, strip trailing / and query params. Example: https://www.linkedin.com/in/some-person/ -> some-person.
3. Set paths:
   • SCRAPER = ~/Desktop/AIC/plugins/linkedin-intel/linkedin-scraper.py
   • COOKIES = ~/Desktop/AIC/plugins/linkedin-intel/.cookies.json
   • RAW_DATA = ~/Desktop/AIC/linkedin/profiles/{slug}/linkedin_raw_data.json
   • OUTPUT = ~/Desktop/AIC/linkedin/profiles/{slug}/notes/profile-report.md
4. Create directories: ~/Desktop/AIC/linkedin/profiles/{slug}/notes/ (use mkdir -p).

IMPORTANT: Before running the scraper, check if linkedin_raw_data.json already exists at the RAW_DATA path. If it does, ask the user:

"linkedin_raw_data.json already exists for {slug}. Use existing data or re-scrape?" If they say use existing, skip Step 2 entirely.

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Step 2: Run Scraper

Run the Playwright scraper via Bash:

cd ~/Desktop/AIC/plugins/linkedin-intel && python linkedin-scraper.py --mode profile {profile_url} {RAW_DATA} --cookies {COOKIES}

• If exit code != 0, show the error and STOP. Do not proceed with missing data.
• If successful, read linkedin_raw_data.json and report:

Scraper complete
  Profile: {meta.profile_text_length} chars
  Activity: {meta.activity_text_length} chars
  Articles: {meta.articles_text_length} chars
  Status: {scrape_status summary}