tam-builder

Put your AI agent on the growth team.

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项目 README

来源文件：README.md

抓取于 2026年8月21日

AI Skills for Brand Growth

Put your AI agent on the growth team.

Research customers and competitors, analyze what is working, create the next campaign, and learn from the result. Goose Skills gives Claude Code, Cursor, Codex, and other coding agents ready-to-use workflows for ads, social media, content, competitive intelligence, SEO, lead generation, and GTM.

Browse all skills at https://skills.gooseworks.ai

Works with Claude Code · Cursor · Codex

Quick Start
Brand Growth collection
Commands
Skills Catalog
Usage Examples
Building from Source
Skill Metadata Contract
Security & Trust
License

Quick Start

AI Coding Agents (Claude Code, Cursor, Codex, etc)

Paste this into your coding agent (Claude Code, Cursor, or Codex) and it'll set everything up:

Install the Gooseworks skills:

In the terminal, run `npx gooseworks install --all`.

Then run `npx gooseworks login` and it'll open a browser to sign in and set up the tools, then confirm it worked.

The skills can be used with /gooseworks <prompt>

Claude Cowork

Run this command in a terminal first:

npx gooseworks install --all

Then authenticate:

npx gooseworks login

Then make sure you're working inside a local folder on your machine, and then you can use the skills in Cowork like this:

Use /gooseworks skill to generate some ad creatives

Install manually

Prefer to run it yourself? Use the command directly:

npx gooseworks install --all       # All detected agents

This gives your coding agent access to the full catalog of 200+ skills. After installing, just ask your agent to use any skill by name.

If you want a cloud-based AI coworker that already knows all these skills and more, sign up to Gooseworks

Brand Growth collection

The Brand Growth collection is a curated path through the normal Goose Skills catalog for consumer and ecommerce brand work. It is not a separate package or command: install GooseWorks once, then ask /gooseworks for the outcome you need.

Stage	What your agent can do	Example skills
Research	Understand the brand, customers, competitors, audiences, creators, trends, comments, and product demand	`brand-research`, `audience-research`, `comment-mining`, `competitor-social-research`, `influencer-prospecting`, `trend-discovery`, `product-demand-research`
Analyze	Diagnose ads, creator profiles, transcripts, policy risk, landing-page message match, and unusual social performance	`competitor-ad-intelligence`, `creator-profile-teardown`, `transcript-intelligence`, `meta-ads-analyzer`, `meta-ad-policy-checker`, `ad-to-landing-page-auditor`, `outlier-post-finder`
Create	Repurpose research, remix graphic ads, make product photography and social graphics, and animate static images	`content-repurposing`, `remix-graphic-ad-from-reference`, `product-photoshoot`, `goose-graphics`, `animate-image`
Learn and iterate	Bring results back into research and analysis, then decide the next test	Re-run the relevant analysis skill with current performance and audience evidence

ScrapeCreators powers structured public social and ad-library research behind several workflows. Signed-in GooseWorks users access it through the managed first-party proxy and do not need a separate ScrapeCreators key. The user-facing skills turn that source data into a brief, shortlist, analysis, or recommendation instead of returning raw API output.

Browse the Brand Growth collection

After installation, start with:

/gooseworks onboard me

The agent will collect the useful company context for future growth work and finish by asking what you want to do first. Existing users can skip onboarding and keep using /gooseworks exactly as they do today.

Commands

npx gooseworks search "reddit scraping"   # Search the skill catalog
npx gooseworks credits                     # Check your credit balance
npx gooseworks update                      # Update to latest skill version

Skills Catalog

200+ skills across the growth stack, grouped by focus area:

Category	What's inside
Ads	Research, build, and analyze paid campaigns across Meta and Google
SEO	Keyword research, content gaps, SERP analysis, technical audits
Lead generation	Find, enrich, and qualify prospects for your pipeline
Outreach	Draft, personalize, and run outbound across email and social
Content	Blog posts, social content, carousels, video scripts, newsletters
Research	Company, market, and prospect deep-dives
Competitive intel	Track competitor pricing, launches, positioning, and ads
Monitoring	Watch for mentions, signals, and changes across the web
Social	Scrape and analyze social platforms and audiences
Brand	Voice, positioning, and visual brand assets

Browse and search every skill at skills.gooseworks.ai.

Usage Examples

After installing, just ask your coding agent naturally:

"/gooseworks Generate static ad creatives for my brand"
"/gooseworks Use the reddit-post-finder skill to search r/startups"
"/gooseworks Use the apollo-lead-finder skill to find CTOs at AI companies"
"/gooseworks Use the competitor-intel skill to research Acme Corp"
"/gooseworks Use the goose-graphics skill to create a LinkedIn carousel about our launch"

Your agent will search the GooseWorks catalog, download the skill, and run it automatically.

Building from Source

git clone https://github.com/gooseworks-ai/goose-skills.git
cd goose-skills
node scripts/validate-skills.js  # Validate SKILL.md + skill.meta.json contract
node scripts/build-index.js      # Generate skills-index.json
node bin/goose-skills.js list    # Test locally

Skill Metadata Contract

Each skill directory must include:

SKILL.md — Skill documentation and usage guide
skill.meta.json — Machine-readable metadata

skill.meta.json fields:

Field	Required	Description
`slug`	Yes	Unique kebab-case identifier
`category`	Yes	`capabilities`, `composites`, or `playbooks`
`tags`	Yes	String array of category tags
`installation.base_command`	Yes	Install command
`installation.supports`	Yes	Array: `claude`, `codex`, `cursor`
`features`	No	Feature flags
`github_url`	No	Source repository URL
`author`	No	Skill author
`example_prompt`	No	Copyable prompt shown in the catalog and docs for trying the skill

Security & Trust

These skills run inside your coding agent, so it's worth knowing exactly what they do:

Open source & inspectable. Every skill — its SKILL.md instructions and all scripts — lives in this repo under the MIT license. The gooseworks CLI fetches skills at runtime so recipes stay current, but the source you'd run is right here to read, diff, or pin before you run it.
Scripts run locally. Skill scripts execute on your machine and write to /tmp/gooseworks-scripts/, never into your project directory. Only API requests go through GooseWorks servers; review any script before letting your agent run it.
Your agent stays in control. The skills are a tool your agent reaches for when it fits the task (data at scale, sources behind auth, a specific provider) — not a replacement for its built-in web search or fetch on quick lookups. You can read or edit any installed SKILL.md to tune that behavior.
Credentials stay local. Auth is a Bearer token stored at ~/.gooseworks/credentials.json (file mode 0600). Third-party provider keys (Apify, Apollo, etc.) are held server-side — your token never touches them. All network calls are HTTPS.
The MCP server is opt-in. Registering the GooseWorks MCP server is off by default; it only happens if you explicitly run gooseworks install --mcp.

Found something that looks off? Open an issue — we'd rather fix it in public.

License

MIT — see LICENSE for details.

The skill files and CLI in this repository are MIT-licensed. The GooseWorks API they connect to is a separate paid service governed by its own terms.

Built by GooseWorks

Get Started · Report an Issue

其他

中风险

来源需自行核对维护者身份。
未检测到明显脚本安装指令。
可能需要外部 token、网络权限或第三方服务。
未检测到高风险命令。
扫描发现：1 条。

Codex — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gooseworks-ai/goose-skills.git
将 "skills/lead-generation/capabilities/tam-builder" 文件夹复制到 Codex 的 skills 目录中。
重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Codex 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gooseworks-ai/goose-skills.git
将 "skills/lead-generation/capabilities/tam-builder" 文件夹复制到 Claude Code 的 skills 目录中。
重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Claude Code 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gooseworks-ai/goose-skills.git
将 "skills/lead-generation/capabilities/tam-builder" 文件夹复制到 Cursor 的 skills 目录中。
重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Cursor 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gooseworks-ai/goose-skills.git
将 "skills/lead-generation/capabilities/tam-builder" 文件夹复制到 GitHub Copilot 的 skills 目录中。
重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 GitHub Copilot 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/gooseworks-ai/goose-skills.git
将 "skills/lead-generation/capabilities/tam-builder" 文件夹复制到 Windsurf 的 skills 目录中。
重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Windsurf 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Windsurf 让新的 skill 生效。

查看 SKILL.md 原文

name: tam-builder
description: >
  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.
tags: [lead-generation]

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)

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