prospector

/octave:prospector - ICP-Fit Prospecting

Find companies and people that match your Ideal Customer Profile. Uses your library's segments, personas, and Motion ICP cells to search for and score prospects. Returns qualified prospect lists with fit reasoning, recommended approaches, and filter suggestions for scaling in Apollo, Clay, or LinkedIn Sales Navigator.

Usage

/octave:prospector [options]

Options

• --motion <name> - Scope to a specific Motion's ICP cells (persona × segment matrix)
• --segment <name> - Filter by market segment
• --persona <name> - Target specific persona type
• --company <domain> - Find people at a specific company
• --similar-to <domain> - Find companies similar to this one
• --count <n> - Number of results (default: 10)

Examples

/octave:prospector                                    # Interactive mode
/octave:prospector --motion "Enterprise Outbound"    # Use Motion ICP cells
/octave:prospector --segment "Healthcare"            # Healthcare companies
/octave:prospector --persona "CTO" --segment "SaaS"  # CTOs at SaaS companies
/octave:prospector --similar-to stripe.com           # Companies like Stripe
/octave:prospector --company acme.com                # Decision makers at Acme

Instructions

When the user runs /octave:prospector:

Step 1: Determine Search Mode

If no options provided, ask:

What kind of prospects are you looking for?

1. Companies that fit a Motion's ICP cells (persona × segment matrix)
2. People at a specific company
3. Companies similar to a reference account
4. Open search (I'll help you define criteria)

Your choice:

Step 2: Gather ICP Criteria

Use MCP tools to gather ICP criteria from your library:

From Motion / Motion ICP cells:

list_motions()
list_motion_icps({ motionOId: "<motion_oId>" })
find_motion_icp({ motionIcpOId: "<motion_icp_oId>", includeLearnings: true })

Extract from the Motion ICP cell narrative (Target ICP overview, Operating landscape, Strategic narrative):

• Target segment characteristics (firmographics, industry, signals)
• Buyer persona titles, seniority, department
• Pains and consequences (invert to search signals — companies showing these pains)
• Methodology cues (engagement triggers, qualifying signals)

From Segment:

get_entity({ oId: "<segment_oId>" })

Extract:

• Firmographic criteria (size, industry, location)
• Common characteristics
• Segment-specific signals

From Persona:

get_entity({ oId: "<persona_oId>" })

Extract:

• Common job titles
• Seniority level
• Department/function

Step 3: Build Search Criteria

Translate library criteria to search parameters:

Building Search Criteria
========================

From your library, I'll search for:

Company Criteria:
- Industry: SaaS, Technology
- Size: 100-1000 employees
- Stage: Series A+
- Location: US, UK, Canada

Person Criteria:
- Titles: CTO, VP Engineering, Head of Engineering
- Seniority: VP+
- Department: Engineering, Technology

Derived from:
- Segment: "Scaling SaaS Companies"
- Persona: "CTO - Enterprise Tech"
- Motion: "Enterprise Outbound — DevOps"
- Motion ICP cell: "CTO × Scaling SaaS"

Proceed with this search? (or adjust criteria)

Step 4: Execute Search

For Company Search:

find_company({
  industry: "<industry>",
  employeeCount: { min: X, max: Y },
  keywords: ["<relevant keywords>"],
  limit: 10
})

For Person Search:

find_person({
  searchMode: "people",
  fuzzyTitles: ["CTO", "VP Engineering"],
  companyDomain: "<domain>",  // if specified
  employeeCount: { min: X, max: Y },
  industry: "<industry>",
  limit: 10
})

For Similar Companies:

find_similar_companies({
  referenceCompany: { domain: "<domain>" },
  numResults: 10,
  similarityTraits: ["industry", "size", "business_model"]
})

For People at Company:

find_person({
  searchMode: "people",
  companyDomain: "<domain>",
  fuzzyTitles: ["<titles from persona>"],
  limit: 10
})

Step 5: Score and Present Results

For each result, calculate ICP fit:

Company Scoring:

qualify_company({
  companyDomain: "<domain>",
  additionalContext: "Evaluating fit for [Motion / Motion ICP cell / segment]"
})

Person Scoring:

qualify_person({
  person: { linkedInProfile: "<url>" },
  additionalContext: "Evaluating fit for [persona] in [Motion ICP cell]"
})

Present results:

See results-output.md for the prospect results template.

Step 6: Generate Filter Recommendations

After presenting results, provide filters for scale:

See scale-filters.md for the scale-search filter template (Apollo, Clay, LinkedIn Sales Navigator, ideal signals).

Step 7: Deep Dive Options

Offer to go deeper on specific prospects:

Research Company:

enrich_company({ companyDomain: "techcorp.com" })

Present enriched data with:

• Full company profile
• Recent news and events
• Key people and org structure
• Technology stack
• Funding history
• Growth signals

Find Contacts:

find_person({
  searchMode: "people",
  companyDomain: "techcorp.com",
  fuzzyTitles: ["<persona titles>"],
  limit: 5
})

Then for each:

enrich_person({
  person: { linkedInProfile: "<url>" }
})

Generate Outreach: Suggest running /octave:generate email or /octave:research for selected prospects.

ICP Criteria Translation

Library Concept	Search Parameter
Segment firmographics	Industry, employee count, location
Segment characteristics	Keywords, technologies
Persona job titles	fuzzyTitles, exactTitles
Persona seniority	Seniority filter
Motion ICP cell pains	Invert as search signals (companies exhibiting these pains)
Motion ICP cell methodology	Engagement triggers, qualifying signals
Product fit criteria	Technology stack, business model

MCP Tools Used

Search Operations

• find_company - Company search with filters
• find_person - People search with filters
• find_similar_companies - Lookalike company search
• find_similar_people - Lookalike people search

Enrichment Operations

• enrich_company - Full company intelligence
• enrich_person - Full person intelligence
• qualify_company - ICP scoring for company
• qualify_person - ICP scoring for person

Library Context

• list_motions - List Motions in the workspace
• list_motion_icps - List Motion ICP cells (persona × segment) for a Motion
• find_motion_icp - Fetch a Motion ICP cell narrative + Learning Loop learnings (drives ICP criteria)
• get_entity - Get segment/persona details
• search_knowledge_base - Find relevant messaging

Output Modes

Default: Interactive

Shows results with scoring, asks for next steps.

List Mode: --format list

Compact list format for quick scanning:

Companies (10 results)
=====================
1. TechCorp (techcorp.com) - 92/100 - SaaS, 450 emp
2. DataFlow (dataflow.io) - 85/100 - SaaS, 230 emp
3. CloudBase (cloudbase.com) - 78/100 - Infra, 180 emp
...

Export Mode: --format csv

Outputs CSV-compatible format:

Company,Domain,Score,Industry,Employees,Location,Recommended Motion ICP Cell
TechCorp,techcorp.com,92,SaaS,450,San Francisco,Enterprise Outbound — CTO × Scaling SaaS
DataFlow,dataflow.io,85,SaaS,230,New York,Growth Outbound — CTO × Growth SaaS
...

Error Handling

No Results:

No companies found matching your criteria.

Try:

1. Broadening the search (larger employee range, more industries)
2. Removing specific filters
3. Using similar-to search with a known good-fit company

Current filters: [show active filters]

Missing Motion Context:

Motion "[name]" not found in your workspace.

Available Motions:

• Enterprise Outbound
• SMB Quick Close
• Healthcare Vertical

Or run /octave:audit to see your library coverage.

API Limits:

Search returned maximum results. Narrow your criteria for more targeted results.

Suggestions:

• Add industry filter
• Specify location
• Use tighter employee range

Related Skills

• /octave:research - Deep dive on specific prospects
• /octave:generate - Create outreach for prospects
• /octave:brainstorm - Generate campaign ideas for prospect lists
• /octave:audit - Ensure library has good ICP definitions
• /octave:abm - Full account plan for top prospects
• /octave:icp-refine - Refine ICP definitions from deal data
• /octave:qual-doctor - Tune the qualification scoring that powers prospector's ICP filters