name: discovery-engine
description: |
Find relevant people for outreach using Exa AI search capabilities.
Supports LinkedIn search, company research, and cross-platform profile discovery.
Use this skill when the user wants to find people based on topics, roles, companies, or industries.
allowed-tools:
- Bash
- Read
- Write
- Glob
- Grep
- TodoWrite
- AskUserQuestion
- WebSearch
- WebFetch

Discovery Engine Skill

Finds relevant people for outreach using Exa AI integration.

When to Use This Skill

Use this skill when the user:
- Wants to find people to reach out to based on criteria
- Searches for people by role, company, industry, or location
- Needs to research companies before outreach
- Wants to build a target list for campaigns

When NOT to Use This Skill

Do NOT use this skill for:
- Sending messages → use workflow-engine or platform-specific skills
- Managing team → use team-manager
- Reading emails → use inbox-reader

Capabilities

1. People Search - Find people using Exa AI LinkedIn search
2. Company Research - Research companies for targeting
3. Profile Enrichment - Cross-reference to find Twitter/Instagram
4. Target Management - Store and organize discovered people
5. Export - Export to JSON/CSV for campaigns

Exa AI MCP Integration

This skill uses Exa AI MCP for intelligent search. The following Exa tools are available:

• linkedin_search_exa - Search LinkedIn for people
• company_research_exa - Research companies
• web_search_exa - General web search for profiles
• deep_researcher_start/check - Deep research on topics

Note: Ensure Exa AI MCP is configured in Claude Code settings.

Discovery Workflow

1. Create Discovery Session

Before searching, create a session to track results:

python .claude/scripts/discovery_engine.py session --query "AI startup founders San Francisco" --source linkedin_search

2. Perform Exa AI Search

Use Exa AI MCP to search (this happens through Claude's MCP integration):

Example prompt to Exa:
"Search LinkedIn for AI startup founders in San Francisco who have raised Series A funding"

3. Process and Store Results

After receiving Exa results, add people to the session:

# The discovery_engine.py processes Exa responses
# Results are stored in output/discovery/people.json

4. View Discovered People

python .claude/scripts/discovery_engine.py list --has-linkedin

5. Export for Campaign

python .claude/scripts/discovery_engine.py export --format csv --output output/discovery/targets.csv

Commands Reference

View Discovery Stats

python .claude/scripts/discovery_engine.py stats

List Discovered People

# All people
python .claude/scripts/discovery_engine.py list

# Filter by criteria
python .claude/scripts/discovery_engine.py list --query "founder" --has-linkedin --status not_contacted

Get Person Details

python .claude/scripts/discovery_engine.py get --id PERSON_ID

Update Outreach Status

python .claude/scripts/discovery_engine.py status --id PERSON_ID --status contacted --notes "Sent LinkedIn connection"

Export People

# JSON export
python .claude/scripts/discovery_engine.py export --format json --output output/targets.json

# CSV export
python .claude/scripts/discovery_engine.py export --format csv --output output/targets.csv

Example Discovery Workflow

User: "Find 10 DevOps engineers at Series B startups in Austin"

Assistant Workflow:

1. Use TodoWrite to track:
   json [ {"content": "Create discovery session", "status": "in_progress"}, {"content": "Search using Exa AI", "status": "pending"}, {"content": "Process and store results", "status": "pending"}, {"content": "Show discovered people", "status": "pending"} ]

2. Create session:
   bash python .claude/scripts/discovery_engine.py session --query "DevOps engineers Series B startups Austin" --source linkedin_search

3. Search with Exa AI:
   Use WebSearch or Exa MCP:
   "Search LinkedIn for DevOps engineers at Series B funded startups in Austin, Texas"

4. Process results:
   Parse Exa response and add people to discovery session.

5. Show summary:
   ```
   Found 10 DevOps engineers:

6. John Smith - Senior DevOps at TechCo
   LinkedIn: linkedin.com/in/johnsmith
   Twitter: @john_devops

7. Jane Doe - Platform Engineer at StartupX
   LinkedIn: linkedin.com/in/janedoe
   ...
   ```

8. Ask user:
   "Would you like to add these 10 people to a workflow for outreach?"

Data Storage

Discovery data is stored in:
- output/discovery/sessions.json - Search sessions
- output/discovery/people.json - All discovered people

Search Best Practices

1. Be Specific: "AI startup founders Series A San Francisco" > "startup founders"
2. Include Location: Helps narrow results
3. Add Context: Role + Industry + Company stage
4. Use Tags: Tag people for easy filtering later

Enrichment

After initial discovery, you can enrich profiles:

1. Find Twitter: Search web for "[Name] [Company] twitter"
2. Find Email: Look for public email on LinkedIn or company site
3. Company Research: Use company_research_exa for company context

The discovery engine automatically deduplicates and merges enriched data.