LinkedIn Leads — Advisory Board

Find and rank potential advisors from a LinkedIn network based on your company's expertise gaps and stage.

Instructions

Step 1: Gather information

Ask the user for everything you need. If they provided details in $ARGUMENTS, use those and only ask for what's missing:

1. Target person's name — A short label used as the output folder (e.g. "eduardo", "aum")
2. LinkedIn URL — The connections page or search results URL to scrape
3. Scrape type — "connections" (their connections list) or "search" (a search results page)
4. What gaps are you filling? — What expertise or experience does your advisory board need? Examples:
   • "Enterprise sales leadership — someone who's built and led a $50M+ ARR sales org"
   • "AI/ML technical depth — published researchers or former ML leads at top labs"
   • "Regulatory and compliance — fintech or healthtech compliance veterans"
5. Company stage context — Brief description of your company so we can evaluate advisor fit (e.g. "Series A AI security startup", "Pre-seed dev tools company"). This helps distinguish advisors who are right for your stage vs. those better suited for later-stage companies.

Step 2: Check for existing data

Look for existing scraped data:

${CLAUDE_SKILL_DIR}/../../output/<person>/meta.json

If data exists, tell the user:

"I found existing data for [person] — [count] profiles scraped on [date] from [url]. Want to use this data or re-scrape?"

If no data exists, proceed to scraping.

Step 3: Scrape (if needed)

Before invoking the scraper, set expectations:

Starting scrape of [person]'s [connections/search results]

• A Chromium browser window will open (this is expected)
• Typical scrape time: 10-15 minutes depending on connection count (one-time — you won't need to re-scrape to run different analyses)
• You'll see real-time progress in the terminal

Invoke the scrape skill:

/linkedin:scrape <person> <type> <url>

Wait for it to complete.

Step 3.5: Post-scrape report

After scraping completes, read ${CLAUDE_SKILL_DIR}/../../output/<person>/progress.json and display:

Scrape complete: [count] profiles in [duration] ([rate] profiles/min) Starting advisor analysis...

Step 4: Analyze

Build the criteria string from the user's gaps and stage context, then invoke the analyze skill in advisors mode:

/linkedin:analyze <person> <gaps + stage context as criteria> advisors

Step 5: Summary

After analysis completes, provide a final wrap-up:

Advisory search complete

• Scrape: [count] profiles in [duration]
• Analysis: [agent_count] agents, ~[tokens]K tokens, [duration]
• Total wall time: ~[total]
• Results: [tier1_count] Tier 1 / [tier2_count] Tier 2 / [tier3_count] Tier 3

Then:

• Top 5 advisor candidates and what makes each one valuable
• File locations for the full ranked list
• Offer to format results for WhatsApp, email, or CSV if the user wants to share them