Prospect Company Research

SKILL.md — Prospect Company Research

Research target companies using PinchTab browser automation to find engineering pain signals, decision-maker context, and product-market fit evidence.

When to Use

• Researching a specific industry vertical for ELK/platform engineering pain
• Vetting a company before adding prospects to the CRM
• Finding engineering blog posts, case studies, or job postings that reveal pain points
• Confirming a company runs self-managed ELK (not Elastic Cloud)

Tools

• PinchTab (pinchtab-mcp): Full browser automation with navigate, get_text, find, click, scroll
• LinkedIn MCP (linkedin-mcp): get_company_overview, get_company_profile, search_people (for company size validation)

Research Sources (Priority Order)

1. Elastic.co Customers (HIGHEST VALUE)

Elastic publishes detailed case studies with exact pain language, cluster sizes, and cost savings.

Navigate to: https://www.elastic.co/customers

Look for: TVH, BBVA, Brazilian Telecom, ID Logistics — all confirmed ELK customers with quantified pain.

For a specific company: https://www.elastic.co/customers/{company-slug}

What to extract:

• Cluster size / data volume
• Exact pain points in their own words
• Cost/savings metrics
• Decision-maker name and title (sometimes listed)
• Whether they're on-prem or cloud

2. Engineering Blogs (HIGH VALUE)

Companies like Discord, Stripe, Shopify publish detailed post-mortems and architecture posts.

Navigate to: https://{company}.com/blog or https://engineering.{company}.com

Use PinchTab find to search for keywords: "ELK", "Elasticsearch", "observability", "logging", "pipeline", "Kafka", "indexing"

Discord's engineering blog is particularly valuable — they have a post "How Discord Indexes Trillions of Messages" with detailed ELK pain:

• URL: https://discord.com/blog/how-discord-indexes-trillions-of-messages
• Pain signals: Redis queue failures, bulk indexing fault tolerance, hitting Lucene MAX_DOC limits (~2B docs), multi-cluster migrations

3. Reddit (MEDIUM VALUE — Noisy)

Best subreddits for ELK pain:

• r/elasticsearch — ELK-specific pain, certifications, configuration issues
• r/devops — Platform engineering pain, observability
• r/sysadmin — Infrastructure logging issues

Navigate to: https://www.reddit.com/r/elasticsearch/search/?q=elk+stack+{keyword}&sort=new

Limitation: Reddit search results are often dominated by certification exam posts and vendor discussion. Filter for technical pain posts. Posts older than 90 days are cold leads.

4. Job Postings (MEDIUM VALUE)

Look for job postings that reveal understaffing or pain:

Navigate to: https://www.linkedin.com/work/search?keywords=Senior+Platform+Engineer+ELK+{company}

Or search: site:linkedin.com OR site:greenhouse.io OR site:lever.co "ELK" "Platform Engineer" "{company}"

Pain signals in job posts:

• "Experience with ELK stack" (they're dealing with it)
• "On-call rotation" (they're stretched)
• "Debug production issues" frequency mentioned
• Hiring multiple platform engineers (growth/understaffing)

5. Glassdoor / Company Reviews (LOW-MEDIUM VALUE)

Navigate to: https://www.glassdoor.com/Reviews/{company}-reviews.htm

Search for: "ELK", "Elasticsearch", "logging", "on-call", "platform team"

Research Checklist

For each company, capture:

• Company name and employee count
• Do they run self-managed ELK or Elastic Cloud? (self-managed = our target)
• Cluster size / data volume if mentioned
• Exact pain language in their own words
• Decision-maker name and LinkedIn profile if found
• Last activity / hiring signals
• Priority score: High / Medium / Low

Common Pitfalls

• Web search is blocked on most sites — don't rely on web_search for deep research. Use PinchTab to navigate directly.
• Reddit search is noisy — cert exam posts dominate. Use company/engineering blog research instead.
• LinkedIn search filters (like site:linkedin.com/in) often don't work. Use LinkedIn MCP search_people with keywords instead.
• Don't over-research — the goal is enough signal to add a prospect and send a connection request. Deep dive on the most promising 2-3 companies per vertical.

Output Format

Return findings as:

{
  "companies_researched": [
    {
      "name": "...",
      "size": "...",
      "self_managed_elk": true,
      "pain_signals": ["exact quote from source"],
      "decision_makers": [{"name": "...", "title": "...", "linkedin": "..."}],
      "priority": "High",
      "source": "elastic.co/customers | engineering blog | reddit | job posting"
    }
  ],
  "top_pain_themes": ["theme 1", "theme 2"],
  "recommended_companies_for_outreach": ["company 1", "company 2"]
}