decision-maker-finder

name	decision-maker-finder
description	Find decision makers at target companies.
metadata	{"author":"Alfonso Sainz de Baranda (Growth4U)","version":"1.0","system":"SanchoCMO","phase":"Encuentra (one-to-one)","depends_on":"company-finder","chains_to":"contact-enrichment"}
context_required	["brand/{slug}/company-brief/company-brief.current.md","brand/{slug}/go-to-market/ecps/ecps.current.md","brand/{slug}/go-to-market/ecps/ecps.current.md"]
context_writes	["campaigns/","brand/{slug}/operational/assets.md"]

Decision Maker Finder — Find the Person Who Can Say YES

"No vendas a empresas. Vende a personas dentro de empresas." — Sancho "The company doesn't buy. A human with a budget and a problem does."

Este skill convierte una lista de empresas target en una lista de personas concretas: el decision maker que puede decir YES a tu producto. No buscamos emails (eso es contact-enrichment). Buscamos quienes son, validamos que son los correctos, y priorizamos por probabilidad de respuesta.

Diferencia con skills vecinos:

• company-finder: Encuentra empresas target (COMPANIES)
• decision-maker-finder: Encuentra personas dentro de esas empresas (PEOPLE)
• contact-enrichment: Encuentra emails y datos de contacto (CHANNELS)

Read ./brand/ per _system/intelligence/brand-memory.md (if using SanchoCMO framework)

Follow _system/output/output-format.md (if using SanchoCMO framework)

---

Prerequisites

Required input:

• Companies list from company-finder (brand/{slug}/operational/companies-YYYYMMDD.json or manual list)
• At minimum: company name + domain + employee count

Tools needed (in priority order):

1. LinkedIn Sales Navigator (best: advanced filters, lead lists)
2. Apollo.io (good: email + role search, free tier available)
3. Apify MCP Server (remote, web-hosted compatible)
   • URL: https://mcp.apify.com/
   • Actors: linkedin-profile-scraper, apollo-scraper
   • Auth: OAuth (Apify API token)
4. WebSearch (fallback: manual search)

Optional context:

• brand/{slug}/go-to-market/ecps.json (buyer personas with target roles)
• ./brand/{slug}/go-to-market/positioning//.current.md (to identify relevant departments)
• brand/{slug}/market-and-us/competitors.json (to identify warm paths via mutual connections)

---

Workflow

Step 0: Tool Detection (Automatic)

Check available tools:

IF LinkedIn Sales Navigator access available:
  mode = "SALES_NAV" (highest quality)
  notify = "LinkedIn Sales Navigator detected — premium search"
ELIF Apollo.io API configured:
  mode = "APOLLO" (good automation)
  notify = "Apollo.io connected — automated people search"
ELIF mcp.apify.com is configured with valid API token:
  mode = "APIFY" (actor-based)
  notify = "Apify MCP connected — using scraper actors"
ELSE:
  mode = "LIGHT" (manual WebSearch + LinkedIn manual)
  notify = "No specialized tools — using manual search workflow"

Present:

TOOL DETECTION

|- Tool: LinkedIn Sales Navigator / Apollo / Apify / Manual
|- Mode: SALES_NAV / APOLLO / APIFY / LIGHT
|- Companies to search: [N] from company-finder
|- Estimated time: 15 min (auto) / 3-5 hours (manual)

Proceed?

---

Step 1: Load Companies

Read from Context Lake (if SanchoCMO):
  brand/{slug}/operational/companies-YYYYMMDD.json

Extract per company:
  |- Company name
  |- Domain
  |- Employee count (for seniority targeting)
  |- Industry (for title variations)
  |- ICP segment (for role prioritization)
  |- LinkedIn company URL (if available)

If NO companies list exists:
  ERROR: Run /company-finder first
  OR: Provide a manual list of companies

Present:

LOADED — [N] Companies from company-finder

Company A (SaaS, 250 employees)
  |- Domain: example.com
  |- LinkedIn: linkedin.com/company/example
  |- ICP: Mid-Market B2B SaaS

Company B (Fintech, 80 employees)
  ...

Companies to search: All [N] / Select specific?

---

Step 2: Define Target Roles

Using role-mapping.md + buyer persona from ECPs:

From ECP buyer persona:
  |- Primary role: VP Marketing
  |- Department: Marketing
  |- Seniority: VP-Level (mid-market deal)

Expand to search aliases:
  |- "VP Marketing"
  |- "Head of Marketing"
  |- "Marketing Director"
  |- "VP Growth"
  |- "Head of Growth"
  |- "CMO" (at smaller companies)

Apply seniority decision tree:
  Company size 250 employees + Deal $20K
  -> Target: VP-Level (primary) + Director-Level (backup)

Present:

TARGET ROLES (Based on ECPs + Company Sizes)

Primary roles (search first):
  1. VP Marketing / Head of Marketing
  2. VP Growth / Head of Growth
  3. CMO (for companies <100 employees)

Backup roles (if primary not found):
  4. Director of Marketing / Director Demand Gen
  5. Marketing Manager / Growth Manager

Aliases included: 12 title variations per role

Adjust roles? Or proceed with these?

---

Step 3: Search Decision Makers

For each company, search using selected tool:

SALES_NAV mode: Filter by Company + Title + Seniority. Sort by connection degree. Extract: Name, title, LinkedIn URL, mutual connections.

APOLLO mode: Search by company domain + title keywords + seniority. Output: Name, title, LinkedIn URL, company, department.

APIFY mode: Actor linkedin-people-search with company + role keywords, maxResults: 10. Output: JSON with profiles.

LIGHT mode: Manual search per company:

1. WebSearch: "[company name]" "[target role]" site:linkedin.com
2. Check company website /about or /team page
3. Cross-reference LinkedIn results (~5-10 min per company)

Progress tracking:

SEARCHING — [N] Companies

[####------] 4/10 companies searched

Company A: Found VP Marketing (Jane Doe) + Director Growth (John Smith)
Company B: Found CMO (Maria Garcia) — small company, CMO = VP-level
Company C: Searching...
Company D: Pending...

---

Step 4: Validate Profiles

Using validation-criteria.md:

For each candidate found:

VALIDATE — Jane Doe (VP Marketing @ Company A)

Profile Quality:
  |- Photo: Yes (+1)
  |- Connections: 2,400 (+1)
  |- Recent activity: Posted 3 days ago (+2)
  |- Complete history: Yes (+1)
  |- Skills endorsed: 27 (+1)
  |- Recommendations: 8 (+1)
  |- Custom headline: Yes (+1)
  |- Creator mode: No (+0)
  Profile Quality Score: 8/9 (Excellent)

Relevance:
  |- Title exact match: "VP Marketing" = Yes (+3)
  |- Department match: Marketing = Yes (+2)
  |- Seniority match: VP for mid-market = Yes (+2)
  |- Active profile: Yes (+1)
  Relevance Score: 8/10

Red Flags: None
Warnings: None

Warm Path:
  |- 2nd degree via Carlos Lopez (mutual connection) (+2)
  |- Shared group: "SaaS Growth Spain" (+1)
  Warm Path Score: +3

COMPOSITE SCORE: 8 + 2 (quality bonus) + 3 (warm path) = 13/15

---

Step 5: Score & Prioritize

Composite Score Formula:

COMPOSITE = Relevance (1-10) + Quality Bonus + Warm Path Bonus

Quality Bonus:
  - Profile 8-9: +2
  - Profile 6-7: +1
  - Profile 4-5: +0
  - Profile 0-3: -2

Warm Path Bonus:
  - 1st degree: +3
  - 2nd degree: +2
  - Shared group: +1
  - No connection: +0

Maximum: 15  |  Primary threshold: 8+  |  Backup threshold: 5+

Rank all candidates per company:

SCORING — Company A

#1 Jane Doe (VP Marketing)
   Composite: 13/15  |  Role: primary

#2 John Smith (Director Growth)
   Composite: 9/15   |  Role: backup

#3 Mike Johnson (Marketing Manager)
   Composite: 6/15   |  Role: backup (lower seniority)

---

Step 6: Select Top Contacts (1-3 per Company)

Selection rules:

• 1 Primary: Highest composite score, correct seniority
• 1-2 Backups: Next highest scores, ideally different seniority level
• Multi-thread for enterprise: Primary (economic buyer) + Champion (user) + Technical evaluator

SELECTION — Company A

PRIMARY: Jane Doe
  |- VP Marketing | Score: 13/15 | Warm path via Carlos Lopez

BACKUP 1: John Smith
  |- Director Growth | Score: 9/15 | No warm path

BACKUP 2: (none — 2 contacts sufficient for mid-market)

---

Step 7: Present Results

DECISION MAKER FINDER — Resultados

Empresas buscadas: 10
Contactos encontrados: 24
Contactos validados: 19
Primary seleccionados: 10 (1 per company)
Backup seleccionados: 8

TOP RESULTS (By Company)

1. Company A (SaaS, 250 emp)
   PRIMARY: Jane Doe — VP Marketing
   |- Score: 13/15 | Quality: Excellent | Warm path: 2nd via Carlos Lopez
   |- LinkedIn: linkedin.com/in/janedoe
   |- Last active: 3 days ago
   BACKUP: John Smith — Director Growth
   |- Score: 9/15 | Quality: Good | No warm path

2. Company B (Fintech, 80 emp)
   PRIMARY: Maria Garcia — CMO
   |- Score: 11/15 | Quality: Good | Warm path: Shared group
   WARNING: "Open to Work" badge — may be leaving

... (remaining companies)

WARNINGS:
- Company B: Maria Garcia has "Open to Work" — consider backup
- Company F: No decision maker found for target role — try different seniority

NEXT STEPS:
1. Review contacts and approve
2. Run /contact-enrichment to get emails + phone
3. Start outreach sequence

Save results? [Y/N]

---

Step 8: Save Output

Write to: brand/{slug}/operational/decision-makers-YYYYMMDD.json

{
  "date": "2026-02-21",
  "source": "decision-maker-finder",
  "tool_used": "apollo",
  "target_roles": ["VP Marketing", "Head of Growth", "CMO"],
  "contacts": [
    {
      "name": "Jane Doe",
      "title": "VP Marketing",
      "company": "Example Corp",
      "company_domain": "example.com",
      "linkedin_url": "linkedin.com/in/janedoe",
      "seniority": "VP",
      "department": "Marketing",
      "relevance_score": 8,
      "profile_quality": "high",
      "profile_quality_score": 8,
      "composite_score": 13,
      "last_active": "2026-02-18",
      "warm_path": "2nd degree via Carlos Lopez",
      "warm_path_score": 2,
      "shared_groups": ["SaaS Growth Spain"],
      "role_type": "primary",
      "red_flags": [],
      "warnings": []
    },
    { "...": "same structure for backup contacts" }
  ],
  "stats": {
    "companies_searched": 10,
    "candidates_found": 24,
    "candidates_validated": 19,
    "primary_selected": 10,
    "backup_selected": 8,
    "warnings_flagged": 2,
    "no_match_companies": 0
  }
}

Append to: ./brand/{slug}/operational/assets.md — Summary with count of primary/backup contacts, avg composite score, warm paths available.

---

LIGHT Mode — Manual Workflow

When no specialized tools are available:

Per Company (5-10 min each):

1. Google Search: "[company name]" "[target role]" site:linkedin.com
2. Company Website: Check /about, /team, /leadership pages
3. LinkedIn Manual: Search company page > People tab > filter by title
4. Cross-Reference: Verify person appears on both LinkedIn AND company site

Validation in LIGHT mode:

• Profile photo: Check LinkedIn profile
• Connections: Visible on public profiles (500+ shows)
• Activity: Check if recent posts visible
• Title: Compare LinkedIn vs company website
• Warm path: Check mutual connections manually

Output in LIGHT mode:

Same JSON format. Mark "tool_used": "manual". Profile quality assessment may be limited (mark "profile_quality": "estimated").

Time estimates:

Companies	LIGHT Mode	FULL Mode
10	1-2 hours	15 min
50	5-8 hours	30 min
100	10-15 hours	1 hour

---

Integration with SanchoCMO Framework

Reads from Context Lake

File	What it provides	How it's used
brand/{slug}/operational/companies-YYYYMMDD.json	Target companies list	Source list for people search
brand/{slug}/go-to-market/ecps.json	Buyer personas with roles	Defines which roles to target
./brand/{slug}/go-to-market/positioning//.current.md	Our value proposition	Identifies relevant departments
brand/{slug}/market-and-us/competitors.json	Competitor data	Warm path identification via shared connections

Writes to Context Lake

File	What it contains
brand/{slug}/operational/decision-makers-YYYYMMDD.json	All validated contacts (structured)
./brand/{slug}/operational/assets.md	Append: Decision makers summary

Chains to

Skill	Relationship
company-finder	INPUT: Provides companies list
contact-enrichment	OUTPUT: Takes decision makers, finds emails + phone
signal-monitor	ENRICHMENT: Provides engagement signals for prioritization
direct-response-copy	OUTPUT: Writes personalized outreach using contact context
email-sequences	OUTPUT: Sequences for validated contacts

Pipeline Position

company-finder → DECISION-MAKER-FINDER → contact-enrichment → email-sequences
   (companies)        (people)                (channels)          (outreach)

---

Reference Files

Read these for detailed criteria:

• role-mapping.md — START HERE — Seniority levels, role aliases, decision tree
• validation-criteria.md — Profile quality signals, relevance scoring, red flags

---

Frequency

Recommended cadence:

• Initial run: After each company-finder batch
• Refresh: Every 60 days (people change roles)
• Ad-hoc: When targeting a specific company for a deal
• Re-validate: Before outreach campaign (check for job changes)

Why regular refresh:

• People change jobs (avg tenure: 2-3 years)
• New hires in target roles (fresh decision makers, open to new tools)
• Warm paths evolve (new mutual connections appear)
• Company restructuring changes reporting lines

---

No contactes a la empresa. Contacta a la persona que puede decir YES.