mcpbeat

Tam Builder

gooseworks-ai/tam-builder

> Build and maintain a scored Total Addressable Market (TAM) using Apollo Company Search. Discovers companies matching ICP, scores fit (0-100), assigns tiers (1/2/3), and auto-builds a persona watchlist for Tier 1-2 companies using Apollo People Search (free). Outputs to CSV.

2k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
1086
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/gooseworks-ai/goose-skills --skill tam-builder

What comes with it

252 bytes besides the instruction
skill.meta.json

The instruction itself

13 sections, as written by the author

TAM Builder

Build and maintain a scored Total Addressable Market. Uses Apollo Company Search to discover companies, scores ICP fit (0-100), assigns tiers (1/2/3), and auto-builds a persona watchlist for Tier 1-2 companies using Apollo People Search (free).

Three modes:

  • build — First-time TAM construction from Apollo search
  • refresh — Update existing TAM: re-score, detect tier changes, deprecate stale companies
  • status — Read-only report of current TAM state

Prerequisites

Apollo API Key

Add to .env:

APOLLO_API_KEY=your-api-key-here

That's it — one env var.

Config Format

Create a JSON config per client/segment:

{
  "client_name": "happy-robot",
  "tam_config_name": "voice-ai-midmarket",

  "company_filters": {
    "organization_num_employees_ranges": ["51,200", "201,500", "501,1000"],
    "q_organization_keyword_tags": ["call center", "contact center"],
    "organization_locations": ["United States"]
  },

  "scoring": {
    "weights": {
      "employee_count_fit": 30,
      "industry_fit": 25,
      "funding_stage_fit": 20,
      "geo_fit": 15,
      "keyword_match": 10
    },
    "tier_thresholds": { "tier_1_min_score": 75, "tier_2_min_score": 50 },
    "target_industries": ["Telecommunications", "Customer Service"],
    "target_employee_ranges": [[51, 200], [201, 500], [501, 1000]],
    "target_funding_stages": ["Series A", "Series B", "Series C"],
    "target_geos": ["United States"]
  },

  "watchlist": {
    "enabled": true,
    "personas_per_company": 3,
    "person_filters": {
      "person_titles": ["VP of Operations", "Head of Customer Service"],
      "person_seniority": ["vp", "director", "c_suite"]
    },
    "tiers_to_watch": [1, 2]
  },

  "mode": "standard",
  "max_pages": 50
}

Approval Gate

CRITICAL: Never export results without explicit user approval.

Required flow:

  • Search Apollo for a small sample first (~100 companies)
  • Score them and present: tier distribution, example Tier 1/2 companies, scoring sanity check
  • Get explicit user approval before running the full build
  • Only then run the full search + score + export

Pipeline: Build Mode

Step 0: --preview → total count + cost estimate (no DB writes)
Step 1: --sample --test → search 1 page, score in-memory, show results (no DB writes)
Step 2: User reviews sample → approves, adjusts filters, or caps scope
Step 3: Full build → Apollo Company Search → Export to CSV → Score → Tier → Watchlist

Phase details (Step 3 only — after user approval):

Phase 1: Apollo Company Search → Upsert raw companies → Score ICP fit → Assign tiers
Phase 2: (skipped in build mode — no prior data to deprecate)
Phase 3: Persona Watchlist — pull 2-3 personas per Tier 1-2 company (free)

Pipeline: Refresh Mode

Phase 1: Apollo Company Search → Upsert/update companies → Re-score → Detect tier changes
Phase 2: Deprecation — companies missing 2+ consecutive refreshes get deprecated
Phase 3: Persona Watchlist — pull personas for new/promoted Tier 1-2 companies,
         disqualify personas at deprecated companies

ICP Scoring (0-100)

Pure function, no API calls. Weighted scoring across 5 dimensions from config:

  • employee_count_fit — headcount in target ranges?
  • industry_fit — industry matches targets?
  • funding_stage_fit — funding stage in targets?
  • geo_fit — HQ location in target geos?
  • keyword_match — org keywords overlap config keywords?

Score thresholds (configurable): >=75 = Tier 1, >=50 = Tier 2, else Tier 3.

Deprecation Rules (refresh only)

  • First miss (not returned by search): metadata.refresh_miss_count = 1, keep active
  • Second consecutive miss: tam_status = 'deprecated'
  • Employee count drops to 0: immediate deprecation
  • Companies with tam_status = 'converted' are always exempt

Watchlist — Persona Sync

| Scenario | Behavior |

|----------|----------|

| New Tier 1-2 company | Pull 2-3 personas immediately |

| Company promoted Tier 3→2 | Pull personas during refresh |

| Company deprecated | Disqualify monitoring personas |

| Company demoted Tier 1→3 | Keep existing personas, stop refreshing |

Mode Caps

| Parameter | Test | Standard | Full |

|-----------|------|----------|------|

| Max pages | 1 | 50 | 200 |

| Max companies | 100 | 5,000 | 20,000 |

Apollo API Reference

  • Company Search: POST https://api.apollo.io/api/v1/mixed_companies/search — Returns matching companies in the accounts array (not organizations). Fields: name, primary_domain, estimated_num_employees, industry, keywords, city, state, country.
  • People Search: POST https://api.apollo.io/api/v1/mixed_people/search$0.01 flat per call (cheapest people search). Returns matching people in the people array. Fields: first_name, title, organization.name. Email/LinkedIn obfuscated on free tier.
  • People Match (enrich): POST https://api.apollo.io/api/v1/people/match — ~$0.03 per match. Reveals email, phone, LinkedIn URL, full name.
  • Auth: x-api-key: {APOLLO_API_KEY} header on all requests
  • Pagination: per_page (max 100), page (1-indexed). pagination.total_entries gives total count.

Output

Save results as CSV to the current working directory:

  • tam-companies-{date}.csv — All discovered companies with ICP score and tier
  • tam-personas-{date}.csv — Persona watchlist for Tier 1-2 companies (from People Search)

How to use it

Copy the folder

Take gooseworks-ai/tam-builder from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.