build-tam

📁 getaero-io/gtm-eng-skills 📅 2 days ago
2
总安装量
2
周安装量
#68826
全站排名
安装命令
npx skills add https://github.com/getaero-io/gtm-eng-skills --skill build-tam

Agent 安装分布

mcpjam 2
claude-code 2
replit 2
junie 2
windsurf 2
zencoder 2

Skill 文档

Build TAM

Use this skill to size and build your total addressable market from ICP filters. Start with a count (virtually free), then pull the actual list.

Step 1: Size your TAM first (virtually free)

Set per_page: 1 — most providers return the total count but only charge for 1 result. This lets you validate your filters before spending credits on a full pull.

deepline tools execute apollo_people_search \
  --payload '{
    "person_titles": ["VP Sales", "Head of Revenue"],
    "include_similar_titles": true,
    "organization_num_employees_ranges": ["51,200", "201,500"],
    "organization_industry_tag_ids": ["technology"],
    "per_page": 1,
    "page": 1
  }' --json

Look for total_people in the response to see your TAM size before pulling.

Step 2: Company-first TAM

# Size first
deepline tools execute apollo_company_search \
  --payload '{
    "q_organization_industry_tag_ids": ["technology"],
    "organization_num_employees_ranges": ["51,200"],
    "per_page": 1,
    "page": 1
  }' --json

# Pull list (100 per page)
deepline tools execute apollo_company_search \
  --payload '{
    "q_organization_industry_tag_ids": ["technology"],
    "organization_num_employees_ranges": ["51,200"],
    "per_page": 100,
    "page": 1
  }' --json

Step 3: Contact-first TAM

deepline tools execute apollo_people_search \
  --payload '{
    "person_titles": ["VP Sales", "CRO", "Head of Revenue Operations"],
    "include_similar_titles": true,
    "organization_num_employees_ranges": ["51,200", "201,1000"],
    "organization_industry_tag_ids": ["technology"],
    "person_locations": ["United States"],
    "per_page": 100,
    "page": 1
  }' --json

Step 4: Enrich your TAM with signals

Once you’ve pulled your TAM, enrich with buying signals before outreach:

deepline enrich --input tam.csv --in-place --rows 0:1 \
  --with 'signals=call_ai_claude_code:{"model":"haiku","json_mode":{"type":"object","properties":{"top_signal":{"type":"string"},"priority":{"type":"string","enum":["high","medium","low"]}},"required":["top_signal","priority"]},"prompt":"Company: {{Company}}\nDomain: {{Domain}}\n\nIs this account showing any buying signals? Return strict JSON."}'

Common ICP filter parameters (Apollo)

Filter Parameter Example values
Job title person_titles ["VP Sales", "Head of GTM"]
Similar titles include_similar_titles true
Headcount organization_num_employees_ranges ["51,200", "201,500"]
Industry organization_industry_tag_ids ["technology", "software"]
Geography person_locations ["United States", "Canada"]
Revenue revenue_range {"min": 1000000, "max": 50000000}

Pagination

Apollo returns up to 100 results per page. For large TAMs:

# Page 1
deepline tools execute apollo_people_search --payload '{"per_page": 100, "page": 1, ...}' --json

# Page 2
deepline tools execute apollo_people_search --payload '{"per_page": 100, "page": 2, ...}' --json

Tips

  • Always check total_people or total_entries with per_page: 1 before pulling
  • Start narrow (tight ICP), validate quality, then widen filters
  • Use person_locations to segment by geo for personalized campaigns
  • After pulling, prioritize with signal discovery before email enrichment

Get started

Sign up and get your API key at code.deepline.com.

npm install -g @deepline/cli
deepline auth login