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: inbound-lead-enrichment
version: 1.0.0
description: >
Fills in missing data for inbound leads — researches the company, identifies the person's
role and seniority, finds other stakeholders at the company, checks for existing CRM
relationships, and updates the lead record. Produces enriched lead data ready for
qualification or outreach. Tool-agnostic.
tags: [lead-generation]Takes inbound leads with incomplete data and fills in the gaps. Researches the company, identifies the person's role, finds other stakeholders at the company, and checks for existing relationships in CRM. Turns a bare email address into a full lead profile.
Load this composite when:
inbound-lead-qualification flags leads as insufficient_datainbound-lead-triage detects leads with missing company/title fields[Raw Leads] → Step 1: Assess Gaps → Step 2: Company Research → Step 3: Person Research → Step 4: Stakeholder Discovery → Step 5: Relationship Check → Step 6: Compile & Output
↓ ↓ ↓ ↓ ↓ ↓
Gap inventory Company profiles Person profiles Buying committee CRM/pipeline matches Enriched lead records
On first run, establish enrichment tool preferences.
{
"enrichment_tools": {
"company_research": {
"primary": "SixtyFour | Orthogonal | web-search",
"secondary": "web-search"
},
"person_research": {
"primary": "SixtyFour | Orthogonal | web-search",
"secondary": "web-search"
},
"stakeholder_finding": {
"primary": "SixtyFour | Orthogonal | web-search",
"secondary": "web-search"
}
},
"crm_source": {
"tool": "HubSpot | Salesforce | CSV | none",
"access_method": ""
},
"buyer_personas": [],
"enrichment_depth": {
"tier_1_leads": "deep",
"tier_2_leads": "deep",
"tier_3_leads": "standard",
"tier_4_leads": "minimal",
"untiered_leads": "standard"
}
}
On subsequent runs: Load config silently.
For each lead, inventory what's known vs. unknown:
Required fields (must fill):
company_name — What company do they work for?company_domain — Company website domainperson_name — Full nameperson_title — Current job titleperson_email — Contact email (usually already have this from inbound)Valuable fields (fill if possible):
company_size — Employee count or rangecompany_industry — Industry classificationcompany_stage — Funding stage or maturitycompany_hq — Headquarters locationcompany_description — One sentence about what they doperson_seniority — IC, Manager, Director, VP, C-Level, Founderperson_department — Engineering, Sales, Marketing, etc.person_linkedin — LinkedIn profile URLperson_tenure — How long at current companyBonus fields (nice to have):
company_tech_stack — Known technologies usedcompany_recent_news — Any recent events (funding, launches, hires)person_background — Previous companies, educationperson_social_activity — Recent posts or engagement topicsFor each lead, classify the enrichment effort needed:
| Gap Level | Missing | Enrichment Needed | Cost |
|---|---|---|---|
| Minimal | 1-2 valuable fields | Quick web search | Free |
| Standard | Company or title missing | Web search + possible API lookup | Low |
| Deep | Multiple required fields missing | Multi-source research | Medium |
| Email-only | Only have an email address | Full research from scratch | High |
"Here's what's missing across your leads. [X] need deep enrichment, [Y] need standard, [Z] just need a quick lookup. Estimated cost: [amount]. Proceed?"
For each unique company in the lead list (deduplicate — don't research the same company twice for multiple leads):
From email domain (if company name is missing):
jane@acme.com → acme.com)Company profile research:
| Field | Primary Source | Fallback Source |
|---|---|---|
| Company name | Domain lookup | Web search |
| Description | Company website (homepage, about page) | LinkedIn company page, web search |
| Employee count | SixtyFour or Orthogonal, LinkedIn company page | Web search |
| Industry | LinkedIn company page, SixtyFour or Orthogonal | Infer from website content |
| Stage/Funding | SixtyFour or Orthogonal, news articles | Web search |
| HQ Location | LinkedIn company page, website | Web search |
| Tech stack | Job postings, BuiltWith | Web search |
| Recent news | Web search (last 90 days) | Twitter/social mentions |
Research depth by config:
Each company gets a company_profile block:
{
"company_name": "",
"company_domain": "",
"company_description": "",
"employee_count": "",
"employee_range": "",
"industry": "",
"sub_industry": "",
"stage": "",
"last_funding": "",
"hq_location": "",
"tech_stack": [],
"recent_news": [],
"research_sources": [],
"confidence": "high | medium | low"
}
If the lead used a personal email (gmail, etc.):
company_unidentified — still proceed with person research if name is availableFor each lead, build a person profile:
From name + company (if title is missing):
Person profile research:
| Field | Primary Source | Fallback Source |
|---|---|---|
| Full name | Input data | LinkedIn profile |
| Current title | LinkedIn profile, SixtyFour or Orthogonal | Web search |
| Seniority level | Infer from title | LinkedIn profile |
| Department | Infer from title | LinkedIn profile |
| Tenure at company | LinkedIn profile | Web search |
| Previous companies | LinkedIn profile | Web search |
| Education | LinkedIn profile | Skip |
| LinkedIn URL | SixtyFour or Orthogonal, web search | Skip |
| LinkedIn headline | LinkedIn profile | Skip |
| Recent activity | LinkedIn posts (if scraper configured) | Skip |
Seniority inference rules:
IC_juniorIC_midIC_seniorManagerDirectorVPC_LevelFounderAdjust for company size:
Each lead gets a person_profile block:
{
"full_name": "",
"current_title": "",
"seniority_level": "",
"department": "",
"tenure_months": null,
"previous_companies": [],
"education": "",
"linkedin_url": "",
"linkedin_headline": "",
"recent_activity_summary": "",
"research_sources": [],
"confidence": "high | medium | low"
}
For each company in the lead list, identify other relevant people — the buying committee.
Why this matters:
Who to find (based on buyer personas from config):
Process per company:
Depth control:
Each company gets a stakeholder_map:
{
"company": "",
"inbound_lead": {
"name": "",
"title": "",
"role_in_deal": "economic_buyer | champion | evaluator | user | unknown"
},
"stakeholders_found": [
{
"name": "",
"title": "",
"seniority": "",
"linkedin_url": "",
"email": "",
"role_in_deal": "",
"relationship_to_lead": "",
"confidence": "high | medium | low"
}
],
"buying_committee_completeness": "full | partial | minimal",
"recommended_multi_thread": ""
}
If the inbound lead IS the economic buyer → stakeholders are supporting context If the inbound lead is a user/evaluator → finding the economic buyer is critical If the inbound lead is unknown → identifying their role determines the multi-threading strategy
For each lead AND each discovered stakeholder, check existing systems for prior relationships:
Check 1 — CRM (HubSpot, Salesforce, CSV):
Check 2 — Outreach history (outreach_log):
Check 3 — Company-level pipeline (companies table or CRM):
Check 4 — Signal history (signals table):
Check 5 — Mutual connections (if data available):
Each lead gets a relationship_context block:
{
"person_in_crm": true/false,
"person_crm_status": "",
"person_outreach_history": [
{
"date": "",
"channel": "",
"campaign": "",
"outcome": ""
}
],
"company_in_pipeline": true/false,
"company_deal_stage": "",
"company_deal_owner": "",
"company_signal_history": [],
"mutual_connections": [],
"relationship_summary": ""
}
Merge all research into a single enriched record per lead:
{
"original_data": {},
"company_profile": {},
"person_profile": {},
"stakeholder_map": {},
"relationship_context": {},
"enrichment_metadata": {
"enrichment_depth": "deep | standard | minimal",
"fields_filled": X,
"fields_still_missing": [],
"sources_used": [],
"confidence_overall": "high | medium | low",
"enrichment_date": "",
"cost_incurred": ""
}
}
Primary: Enriched CSV
Produce a CSV that extends the original lead data with all enriched fields:
| Original Fields | + Company Fields | + Person Fields | + Stakeholder Fields | + Relationship Fields | + Metadata |
|---|---|---|---|---|---|
| All input columns | company_description, employee_count, industry, stage, hq, tech_stack, recent_news | current_title, seniority, department, tenure, linkedin_url, headline | stakeholder_1_name, stakeholder_1_title, stakeholder_1_role, ... (up to 4) | in_crm, crm_status, in_pipeline, deal_stage, outreach_history_summary | enrichment_depth, confidence, fields_missing, sources_used |
Save to the current working directory or wherever the user prefers (e.g., leads/inbound-enriched-[date].csv).
Secondary: Enrichment Report
## Lead Enrichment Report: [Date]
### Summary
- **Total leads enriched:** X
- **Deep enrichment:** X leads (Tier 1-2)
- **Standard enrichment:** X leads (Tier 3)
- **Minimal enrichment:** X leads (Tier 4)
### Data Quality
- **Fully enriched** (all required + valuable fields): X leads
- **Mostly enriched** (all required, some valuable): X leads
- **Partially enriched** (some required fields still missing): X leads
- **Could not enrich** (insufficient starting data): X leads
### Company Research
- **Unique companies researched:** X
- **Companies already in CRM:** X
- **Companies with active deals:** X (flag for deal owner)
- **Companies with signal history:** X
### Stakeholder Discovery
- **Total stakeholders found:** X across Y companies
- **Economic buyers identified:** X
- **Champions identified:** X
- **Full buying committees mapped:** X companies
### Relationship Flags
- **Leads already in CRM:** X (update status, don't create duplicates)
- **Previously contacted leads:** X (check outreach history before re-engaging)
- **Companies with active deals:** X (coordinate with deal owner)
- **Warm intro paths found:** X
### Cost
- **Enrichment tool credits used:** [breakdown by tool]
- **Cost per lead:** [average]
### CSV saved to: [path]
Lead with only an email, nothing else:
enrichment_failed with reason, recommend manual lookupSame company appears multiple times (multiple inbound leads):
Lead claims a title that doesn't match LinkedIn:
Company recently renamed, merged, or was acquired:
Person left the company since filling the form:
Enrichment tool rate limits or failures:
enrichment_partial and move onVery high volume (100+ leads):
Personal email domains:
company_unidentified, enrich person onlyAfter enrichment is complete, update the source systems:
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