Outreach Research

How to use it

Step 1 — User opens the browser and logs in:

openclaw browser start --browser-profile openclaw

Navigate to linkedin.com, log in manually (one time only — session persists in the profile).

Step 2 — User browses LinkedIn:

• Run the search queries the skill generated
• Open profiles that look relevant (Cmd+click to open in new tabs)
• Open posts you want to use as hooks (open in new tabs)
• No need to do anything else — just browse normally

Step 3 — Skill reads what is open:

# List all open tabs
openclaw browser tabs --browser-profile openclaw

# Read a specific tab by its target-id
openclaw browser snapshot --target-id <id> --browser-profile openclaw

The skill scans all open tabs, identifies which are LinkedIn profiles vs. posts vs. search results, and extracts content from each.

Step 4 — Skill enriches all profiles automatically: From a LinkedIn profile page the skill can extract:

• Full name, current title, company
• Location
• About section (self-description in their own words — great for hooks)
• Work history (tenure, career trajectory)
• Recent activity/posts visible on profile

From a LinkedIn post page the skill can extract:

• Full post text
• Post date
• Engagement signals (comment count visible on page)

This replaces Google cache enrichment for any contact the user has opened in a tab. Google cache is the fallback when no tab is open for that person.

Phase 1: Research Brief & Search Query Generation

IMPORTANT: When the user invokes this skill, the first thing you do is ask them about their problem space and outreach phase, then immediately generate tailored LinkedIn search queries for them to use.

Collect from the user:

• Problem space: What problem are they exploring? (e.g., "SOP handling in Pharma", "sales onboarding at SaaS companies", "AI agent evaluation workflows")
• Outreach phase: What phase are they in?
  • Phase A (Problem Discovery): Early exploration, need to understand the pain deeply → target people who FEEL the pain daily
  • Phase B (Validation): Problem understood, validating scope and willingness to solve → target people accountable for the problem
  • Phase C (Decision): Solution direction clear, testing willingness to pay → target budget holders and decision makers
• Company sweet spot: Industry, stage, size range (default: 5-200 employees)
• Geography (optional): Location constraints
• Interview goal: What specific insight do they need?
• Already contacted (optional): Names of people they've already reached out to. Parse into a simple exclusion list of name + company pairs. Skip these throughout the process.

Persona Mapping Framework

The key insight: match search filters to who FEELS the pain, not who OWNS the company.

Before generating searches, determine:

1. Who experiences this problem daily? (the doer)
2. Who gets blamed when it goes wrong? (accountability holder)
3. Who has budget authority to fix it? (decision maker)

Persona tiers by phase:

EXCEPTION - Founder-Led Companies (<50 employees): When the problem space naturally sits with founders/CEOs of small companies (e.g., AI agent tooling, developer tools, early-stage product challenges), search for founders REGARDLESS of phase, but filter by:

• Company size: 5-50 employees (founders still feel operational pain)
• Stage: Seed to Series A (founders still in the weeds)
• Industry match: Their company must work in the problem domain

Search Query Generation

After collecting the brief, immediately generate 3-5 LinkedIn search queries tailored to the problem space and phase.

Query Structure Principles

IMPORTANT: Keep queries BROAD. Over-filtering in the search query returns zero results. Use LinkedIn's UI filters instead.

Winning pattern:

• Search query: 2-3 broad terms maximum
• Refinement: Use LinkedIn's company size, location, industry filters in the UI

For Problem Spaces in Established Companies (Pharma, Manufacturing, Enterprise):

Phase A (Problem Discovery):
"[Job Title]" [Industry]
Example: "Quality Manager" Pharma

Phase B (Validation):
"Head of [Function]" [Industry]
Example: "Head of Quality" Pharma

Phase C (Decision):
"VP [Function]" [Industry]
Example: "VP Quality" Pharma

Then use LinkedIn filters: Company size (50-1000), Location (Germany), etc.

For Founder-Led Problems (Startups, Tech, AI):

All Phases (target founders at small companies):
founder [Industry/Tech]
Example: founder AI
Example: CEO SaaS

Then use LinkedIn filters: Company size (1-50), Location, Industry

Example: SOP Handling in Pharma

User says: "I'm exploring SOP handling in Pharma companies, Phase A (problem discovery)"

You generate:

Phase A - Tier 1 (Problem Discovery):

LinkedIn People Search (copy these one by one):
1. "Quality Manager" Pharma
2. "Regulatory Affairs" Pharma
3. "Compliance Manager" Biotech

Then apply these LinkedIn UI filters:
- Company size: 50-1,000 employees
- Location: Germany (or your target region)
- Current company (to filter out job seekers)

Google alternative (no LinkedIn login):