复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Put your AI agent on the growth team.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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
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>
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
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
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.
npx gooseworks search "reddit scraping" # Search the skill catalog
npx gooseworks credits # Check your credit balance
npx gooseworks update # Update to latest skill version
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.
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.
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
Each skill directory must include:
SKILL.md — Skill documentation and usage guideskill.meta.json — Machine-readable metadataskill.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 |
These skills run inside your coding agent, so it's worth knowing exactly what they do:
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./tmp/gooseworks-scripts/, never into your project directory. Only API requests go through GooseWorks servers; review any script before letting your agent run it.SKILL.md to tune that behavior.~/.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.gooseworks install --mcp.Found something that looks off? Open an issue — we'd rather fix it in public.
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
name: topical-authority-mapper
description: >
Map complete topic clusters for any subject area — hub pages, spoke articles,
supporting content, internal linking architecture. Identifies content gaps,
priority order, and builds a structured content calendar. Produces topic maps
that build genuine topical authority, not random blog posts.
tags: [seo]Most content strategies are just keyword lists turned into blog posts. Real topical authority requires a structured map: pillar pages that own broad topics, cluster pages that go deep on subtopics, and an internal linking architecture that tells Google "we comprehensively cover this subject." This skill builds that map.
Core principle: Google rewards topical depth, not random keyword coverage. A site with 15 interlinked articles that thoroughly cover "sales automation" will outrank a site with 50 unrelated blog posts that happen to mention the phrase. This skill builds the cluster architecture that creates genuine authority.
Topic cluster mapping is significantly better with keyword data that shows search volume, difficulty, and semantic relationships across hundreds of subtopic variations.
"I can build a comprehensive topical authority map using competitive analysis and content gap identification. For the most precise results — especially accurate volume data and keyword clustering at scale — I'd recommend connecting a keyword data API."
Recommended: DataForSEO (pay-per-use, ~$0.01/keyword, no monthly minimum)
- Sign up at dataforseo.com → get API login + password
- Set
DATAFORSEO_LOGINandDATAFORSEO_PASSWORDenv varsAlternatives that also work:
- Keywords Everywhere API ($1 per 10 credits = 100K keywords, very cheap) → set
KEYWORDS_EVERYWHERE_API_KEY- SEMrush API (if you have a subscription) → set
SEMRUSH_API_KEY- Ahrefs API (if you have a subscription) → set
AHREFS_API_TOKEN"Want to use one of these, or should I proceed with baseline mode? Baseline uses our existing SEO tools and web research — still produces a strong topic map, but with less granular volume data per subtopic."
seo-domain-analyzer for domain metrics, web_search for topic research, reddit-post-finder for question mining, competitor analysis via site-content-catalog. Topic mapping and cluster architecture are equally strong. Volume estimates are directional rather than exact.Run site-content-catalog on your site:
python3 skills/site-content-catalog/scripts/catalog_content.py \
--url "<your_site_url>" \
--output json
Map all existing content:
For each competitor, run site-content-catalog:
python3 skills/site-content-catalog/scripts/catalog_content.py \
--url "<competitor_url>" \
--output json
Map their content architecture:
Run seo-domain-analyzer for your site and competitors:
For each target topic area, generate the full subtopic universe:
Enhanced mode (DataForSEO / Keywords Everywhere):
# DataForSEO keyword suggestions
POST /v3/dataforseo_labs/google/keyword_suggestions/live
{
"keyword": "<topic>",
"limit": 500
}
# DataForSEO related keywords
POST /v3/dataforseo_labs/google/related_keywords/live
{
"keyword": "<topic>",
"limit": 500
}
Extract:
Baseline mode:
Use multiple sources to build the subtopic list:
web_search for "topic + [what/how/why/best/vs/guide/examples]"reddit-post-finder for questions people ask about the topicRun reddit-post-finder for each topic area:
python3 skills/reddit-post-finder/scripts/search_reddit.py \
--subreddit "<relevant_subs>" \
--keywords "<topic>" \
--days 365 --sort top --time year
Extract:
Group all discovered keywords/subtopics into semantic clusters:
Enhanced mode: Use DataForSEO keyword clustering API or group by SERP overlap (keywords that share 3+ ranking URLs likely belong to the same cluster).
Baseline mode: Manual semantic grouping based on:
For each topic area, design the cluster hierarchy:
PILLAR: [Broad Topic] — "The Complete Guide to [Topic]"
│
├── CLUSTER 1: [Subtopic Group A]
│ ├── Article: [Specific subtopic A1]
│ ├── Article: [Specific subtopic A2]
│ └── Article: [Specific subtopic A3]
│
├── CLUSTER 2: [Subtopic Group B]
│ ├── Article: [Specific subtopic B1]
│ ├── Article: [Specific subtopic B2]
│ └── Article: [Specific subtopic B3]
│
├── CLUSTER 3: [Subtopic Group C]
│ ├── Article: [Specific subtopic C1]
│ └── Article: [Specific subtopic C2]
│
└── SUPPORTING: [Glossary terms, FAQs, tools]
├── Glossary: [Term 1]
├── Glossary: [Term 2]
└── FAQ: [Common questions]
For each piece in the cluster:
| Content Type | When to Use | Typical Word Count |
|---|---|---|
| Pillar page | Broad topic overview, links to all cluster content | 3,000-5,000+ |
| Cluster article | Deep dive on subtopic | 1,500-3,000 |
| Comparison post | vs/ or alternatives content | 2,000-3,500 |
| How-to guide | Step-by-step instruction | 1,500-2,500 |
| Glossary entry | Definition + context | 500-1,000 |
| Tool/Calculator | Interactive resource | 500 + tool |
| Case study | Proof point | 1,000-2,000 |
| Listicle | Curated collection | 1,500-3,000 |
Design the linking structure:
Map specific anchor text for each link.
| Subtopic | Your Content | Competitor A | Competitor B | Volume | Difficulty | Gap? |
|---|---|---|---|---|---|---|
| [subtopic 1] | ✗ None | ✓ Pillar page | ✓ Blog post | [vol] | [diff] | ✓ High priority |
| [subtopic 2] | ✓ Thin post | ✓ Deep guide | ✗ None | [vol] | [diff] | ✓ Update needed |
| [subtopic 3] | ✓ Strong guide | ✓ Similar | ✓ Similar | [vol] | [diff] | ✗ Covered |
| [subtopic 4] | ✗ None | ✗ None | ✗ None | [vol] | [diff] | ✓ White space |
Score each content piece to create:
| Factor | Weight | Description |
|---|---|---|
| Search volume | 25% | Monthly search demand |
| Competitive gap | 25% | How much better can you be than what exists? |
| Intent alignment | 20% | Does the searcher match your ICP? |
| Cluster completeness | 15% | Does this fill a critical gap in a cluster? |
| Effort | 15% | How much work to create high-quality content? |
Based on content capacity and priority scores:
Month 1: Build [N] pillar foundations
Month 2: Deepen Cluster 1, start Cluster 2
Month 3: Complete Cluster 2, begin Cluster 3
Months 4-6: Expansion
# Topical Authority Map — [Site/Client] — [DATE]
## Executive Summary
- Topic areas mapped: [N]
- Total content pieces identified: [N] (pillars: [N], clusters: [N], supporting: [N])
- Existing content: [N] pages ([N] strong, [N] need updates, [N] gaps)
- Net new content needed: [N] pages
- Estimated timeline to full coverage: [N] months at [N] articles/month
---
## Topic Map: [Topic Area 1]
### Cluster Architecture
[Visual tree structure per Phase 3A]
### Pillar Page
- **Target keyword:** [keyword] ([volume]/mo, [difficulty])
- **Title:** [recommended title]
- **Content type:** Comprehensive guide
- **Word count target:** [X]-[Y]
- **Links to:** [all cluster articles listed]
- **Status:** [Exists — needs update / New — priority [P0/P1/P2]]
### Cluster: [Subtopic Group A]
#### Article: [Subtopic A1]
- **Target keyword:** [keyword] ([volume]/mo, [difficulty])
- **Content type:** [how-to / comparison / listicle / etc.]
- **Word count target:** [X]-[Y]
- **Links to:** Pillar + [related articles]
- **Links from:** Pillar + [related articles]
- **Priority:** [P0/P1/P2]
- **Anchor text:** "[anchor]" from pillar, "[anchor]" from [related article]
#### Article: [Subtopic A2]
...
### Cluster: [Subtopic Group B]
...
---
## Topic Map: [Topic Area 2]
...
---
## Internal Linking Matrix
| From ↓ / To → | Pillar | Article A1 | Article A2 | Article B1 | ... |
|----------------|--------|-----------|-----------|-----------|-----|
| **Pillar** | — | ✓ "[anchor]" | ✓ "[anchor]" | ✓ "[anchor]" | |
| **Article A1** | ✓ "[anchor]" | — | ✓ "[anchor]" | | |
| **Article A2** | ✓ "[anchor]" | ✓ "[anchor]" | — | | |
| **Article B1** | ✓ "[anchor]" | | | — | |
---
## Content Calendar
### Month 1: Foundation
| Week | Content Piece | Type | Cluster | Keywords | Priority |
|------|--------------|------|---------|----------|----------|
| W1 | [Pillar: Topic 1] | Pillar page | — | [kw] ([vol]) | P0 |
| W1 | [Article A1] | Cluster article | A | [kw] ([vol]) | P0 |
| W2 | [Article A2] | Cluster article | A | [kw] ([vol]) | P0 |
| W2 | [Article B1] | Cluster article | B | [kw] ([vol]) | P0 |
### Month 2: Depth
...
### Month 3: Expansion
...
---
## Coverage Gap Report
### High Priority (Competitors rank, you don't)
| Topic | Competitor Coverage | Your Status | Volume | Recommended Action |
|-------|-------------------|-------------|--------|--------------------|
| [topic] | A: Pillar, B: Blog post | None | [vol] | Create [content type] |
### Medium Priority (Weak coverage)
| Topic | Your Current Page | Issue | Volume | Recommended Action |
|-------|------------------|-------|--------|--------------------|
| [topic] | [URL] | Thin (400 words) | [vol] | Expand to [X] words, add [sections] |
### Existing Content Updates Needed
| URL | Issue | Action Required | Effort |
|-----|-------|----------------|--------|
| [url] | Outdated (2023 data) | Update stats, refresh examples | 2 hours |
| [url] | No internal links | Add [N] links to cluster articles | 30 min |
| [url] | Missing from pillar | Add link from pillar with "[anchor]" | 15 min |
---
## Metrics to Track
- **Topical coverage %** — Articles created vs. total identified
- **Internal link density** — Avg links per article within cluster
- **Cluster ranking velocity** — Time from publish to page 1 per cluster
- **Pillar page rankings** — Position for head terms
- **Organic traffic by cluster** — Traffic attributed to each topic cluster
Save to the current working directory or wherever the user prefers.
For large topic maps (3+ topic areas), also export a summary CSV:
content-calendar-[YYYY-MM-DD].csv
| Component | Cost |
|---|---|
| Site catalog (your site, once) | ~$0.05-0.10 |
| Site catalog per competitor | ~$0.05-0.10 |
| SEO domain analyzer | ~$0.10-0.20 |
| Reddit scraper (per topic area) | ~$0.05-0.10 |
| DataForSEO keyword data (enhanced) | ~$0.50-3.00 (depending on keyword count) |
| Keywords Everywhere (enhanced alt) | ~$0.01-0.10 |
| Page fetches (competitor content analysis) | ~$0.01-0.05 |
| Analysis | Free (LLM reasoning) |
| Total per topic area (baseline) | ~$0.25-0.50 |
| Total per topic area (enhanced) | ~$0.75-3.50 |
| 3 topic areas (baseline) | ~$0.75-1.50 |
| 3 topic areas (enhanced) | ~$2.25-10.50 |
APIFY_API_TOKEN env varsite-content-catalog, seo-domain-analyzer, reddit-post-finder, fetch_webpageDATAFORSEO_LOGIN + DATAFORSEO_PASSWORD), Keywords Everywhere (KEYWORDS_EVERYWHERE_API_KEY), SEMrush (SEMRUSH_API_KEY), or Ahrefs (AHREFS_API_TOKEN)For ongoing topical authority tracking:
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