gtm-skills

Phase 1 — Build the Account List

Use Vibe Prospecting to translate your ICP definition into a concrete list of target companies.

Prerequisites: Vibe Prospecting configured (see setup.md)

---

Step 1 — Define Your ICP

Before running any search, gather these inputs from the user:

Dimension	Examples
Industry	SaaS, Fintech, Healthcare IT, E-commerce
Company size	50–500 employees, or $10M–$100M revenue
Geography	US, UK, DACH, APAC
Tech stack	Salesforce, HubSpot, AWS, Snowflake
Funding stage	Seed, Series A–C, PE-backed, Public
Growth signals	Recent funding, hiring surge, new office opening
Intent signals	Actively researching competitor tools
Exclude	Competitors, current customers (get domain list from CRM)

Store the ICP as icp-definition.json at the project root:

{
  "industry_linkedin": ["Software Development", "Financial Services"],
  "headcount_range": ["51-100", "101-250", "251-500"],
  "country_codes": ["US"],
  "tech_stack": ["Salesforce", "HubSpot"],
  "funding_recency_days": 180,
  "exclude_domains": ["competitor.com", "existing-customer.com"]
}

---

Step 2 — Normalize Filter Values

LinkedIn category and NAICS values must come from the Vibe autocomplete, not free text.

# Look up linkedin_category values
npx @vibeprospecting/vpai@latest autocomplete \
  --args '{"field":"linkedin_category","query":"software"}' \
  --tool-reasoning 'Normalizing industry filter for ABM account list'

# Look up tech stack values
npx @vibeprospecting/vpai@latest autocomplete \
  --args '{"field":"company_tech_stack_tech","query":"salesforce"}' \
  --tool-reasoning 'Normalizing tech stack filter for ABM account list'

# Look up city/region if targeting specific metros
npx @vibeprospecting/vpai@latest autocomplete \
  --args '{"field":"city_region","query":"new york"}' \
  --tool-reasoning 'Normalizing geography filter for ABM account list'

Use only the exact values returned by autocomplete in your fetch calls.

---

Step 3 — Sample Fetch (5 accounts)

Always run a sample first. Show results and wait for approval before full export.

npx @vibeprospecting/vpai@latest fetch-entities \
  --args '{
    "entity_type": "business",
    "filters": {
      "linkedin_category": {"values": ["Software Development"]},
      "company_headcount_range": {"values": ["51-100","101-250","251-500"]},
      "company_country_code": {"values": ["US"]},
      "company_tech_stack_tech": {"values": ["Salesforce"]}
    },
    "page_size": 5
  }' \
  --tool-reasoning 'Sample fetch for ABM account list — awaiting user approval'

Show the 5 results in a markdown table with: company name, domain, headcount, revenue, location, tech stack.

Ask the user to confirm before proceeding. Stop the turn here.

---

Step 4 — Full Export

Only after user approval. Export all matching companies to CSV.

# Set writable tmp dir
export TMPDIR=/tmp/abm-vibe
mkdir -p $TMPDIR

# Page 1
npx @vibeprospecting/vpai@latest fetch-entities \
  --args '{
    "entity_type": "business",
    "filters": {
      "linkedin_category": {"values": ["Software Development"]},
      "company_headcount_range": {"values": ["51-100","101-250","251-500"]},
      "company_country_code": {"values": ["US"]},
      "company_tech_stack_tech": {"values": ["Salesforce"]}
    },
    "page_size": 100,
    "page": 1
  }' \
  --save-csv \
  --tool-reasoning 'Full export for ABM account list'

Paginate until you have the desired number of accounts (or the response returns fewer than page_size).

---

Step 5 — Apply Exclusions

Remove competitors and existing customers from the raw list using Python:

import csv

exclude_domains = set(["competitor.com", "existing-customer.com"])  # from icp-definition.json

with open("abm-accounts-raw.csv") as f_in, open("abm-accounts-filtered.csv", "w") as f_out:
    reader = csv.DictReader(f_in)
    writer = csv.DictWriter(f_out, fieldnames=reader.fieldnames)
    writer.writeheader()
    for row in reader:
        domain = row.get("company_domain", "").lower()
        if domain and not any(domain.endswith(d) for d in exclude_domains):
            writer.writerow(row)

print("Done. Remaining accounts:", sum(1 for _ in open("abm-accounts-filtered.csv")) - 1)

---

Step 6 — Add Intent Signal Filter (Optional)

If your ICP includes accounts showing purchase intent or recent business events:

# Fetch companies with recent funding rounds
npx @vibeprospecting/vpai@latest fetch-entities \
  --args '{
    "entity_type": "business",
    "filters": {
      "linkedin_category": {"values": ["Software Development"]},
      "company_country_code": {"values": ["US"]},
      "events": {"values": ["new_funding_round"], "last_occurrence": 180}
    },
    "page_size": 5
  }' \
  --tool-reasoning 'Fetching accounts with recent funding signals for ABM list'

Available event types: new_funding_round, executive_change, product_launch, hiring_surge, expansion_news

---

Outputs

File	Description
icp-definition.json	ICP parameters
abm-accounts-raw.csv	All matching companies from Vibe
abm-accounts-filtered.csv	After exclusions applied

Typical raw list size: 500–5,000 companies. You’ll score and filter down to your final target list in Phase 2.

Next: 02-enrich-score.md

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