Agent skill

Inbound Lead Enrichment

by gooseworks-ai in gooseworks-ai/goose-skills

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…

MITAuto-check passedMarketing & SEO

Install Inbound Lead Enrichment

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/lead-generation/composites/inbound-lead-enrichment .claude/skills/inbound-lead-enrichment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
inbound-lead-enrichment
GitHub stars
1.2k
Used in
1 other repo
Token cost
~4.6k tokens
SKILL.md length
1,740 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Works in 7 steps: Configuration (Once Per Client) → Assess Data Gaps → Company Research → …
  • Tasks that involve Lead generation
  • SKILL.md covers When to Auto-Load, Architecture, Step 0: Configuration (Once… and Step 1: Assess Data Gaps, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Inbound Lead Enrichment is an agent skill from gooseworks-ai/goose-skills. 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.

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Marketing & SEO, covering Lead generation. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Lead generation

Example prompts

  • “Use the inbound-lead-enrichment skill to fill in missing data for inbound leads — researches the company, identifies the person's role and…”
  • “/inbound-lead-enrichment”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Configuration (Once Per Client)
  2. Assess Data Gaps
  3. Company Research
  4. Person Research
  5. Stakeholder Discovery
  6. Relationship Check
  7. Compile & Output

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Inbound Lead Enrichment loads about 4.6k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 1,740 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,740 words, ~4,578 tokens.

Download SKILL.mdSave it as .claude/skills/inbound-lead-enrichment/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
inbound-lead-enrichment
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.
version
1.0.0
tags
lead-generation

Inbound Lead Enrichment

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.

When to Auto-Load

Load this composite when:

  • User says "enrich these leads", "fill in the missing data", "research these inbound leads"
  • inbound-lead-qualification flags leads as insufficient_data
  • inbound-lead-triage detects leads with missing company/title fields
  • User has a list of emails or partial lead data and needs complete profiles

Architecture

[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

Step 0: Configuration (Once Per Client)

On first run, establish enrichment tool preferences.

json
{
  "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.


Step 1: Assess Data Gaps

Process

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 domain
  • person_name — Full name
  • person_title — Current job title
  • person_email — Contact email (usually already have this from inbound)

Valuable fields (fill if possible):

  • company_size — Employee count or range
  • company_industry — Industry classification
  • company_stage — Funding stage or maturity
  • company_hq — Headquarters location
  • company_description — One sentence about what they do
  • person_seniority — IC, Manager, Director, VP, C-Level, Founder
  • person_department — Engineering, Sales, Marketing, etc.
  • person_linkedin — LinkedIn profile URL
  • person_tenure — How long at current company

Bonus fields (nice to have):

  • company_tech_stack — Known technologies used
  • company_recent_news — Any recent events (funding, launches, hires)
  • person_background — Previous companies, education
  • person_social_activity — Recent posts or engagement topics
Gap Classification

For each lead, classify the enrichment effort needed:

Gap LevelMissingEnrichment NeededCost
Minimal1-2 valuable fieldsQuick web searchFree
StandardCompany or title missingWeb search + possible API lookupLow
DeepMultiple required fields missingMulti-source researchMedium
Email-onlyOnly have an email addressFull research from scratchHigh
Output
  • Gap inventory table showing each lead and what's missing
  • Recommended enrichment depth per lead (based on gap level AND urgency tier if available)
  • Cost estimate if paid tools will be used
Human Checkpoint

"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?"


Step 2: Company Research

Process

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):

  1. Extract domain from email (e.g., jane@acme.com → acme.com)
  2. Skip personal email domains (gmail, yahoo, hotmail, outlook, etc.)
  3. Look up the domain → company name, description

Company profile research:

FieldPrimary SourceFallback Source
Company nameDomain lookupWeb search
DescriptionCompany website (homepage, about page)LinkedIn company page, web search
Employee countSixtyFour or Orthogonal, LinkedIn company pageWeb search
IndustryLinkedIn company page, SixtyFour or OrthogonalInfer from website content
Stage/FundingSixtyFour or Orthogonal, news articlesWeb search
HQ LocationLinkedIn company page, websiteWeb search
Tech stackJob postings, BuiltWithWeb search
Recent newsWeb search (last 90 days)Twitter/social mentions

Research depth by config:

  • Deep: All fields, multiple sources, verify across sources
  • Standard: Required + valuable fields, primary source only
  • Minimal: Company name + description + size only
Output

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"
}
Handling Personal Email Domains

If the lead used a personal email (gmail, etc.):

  1. Check if name + any other available data can identify the company (e.g., form field, chat message)
  2. If company is mentioned in their form submission or chat, use that
  3. If truly unknown, flag as company_unidentified — still proceed with person research if name is available

Step 3: Person Research

Process

For each lead, build a person profile:

From name + company (if title is missing):

  1. Search LinkedIn for person at company (via configured tool or web search)
  2. Cross-reference with SixtyFour or Orthogonal
  3. If multiple matches, use email domain to disambiguate

Person profile research:

FieldPrimary SourceFallback Source
Full nameInput dataLinkedIn profile
Current titleLinkedIn profile, SixtyFour or OrthogonalWeb search
Seniority levelInfer from titleLinkedIn profile
DepartmentInfer from titleLinkedIn profile
Tenure at companyLinkedIn profileWeb search
Previous companiesLinkedIn profileWeb search
EducationLinkedIn profileSkip
LinkedIn URLSixtyFour or Orthogonal, web searchSkip
LinkedIn headlineLinkedIn profileSkip
Recent activityLinkedIn posts (if scraper configured)Skip

Seniority inference rules:

  • Titles containing: Intern, Associate, Coordinator, Specialist → IC_junior
  • Titles containing: Analyst, Engineer, Designer, Developer (no "Senior/Lead/Staff") → IC_mid
  • Titles containing: Senior, Lead, Staff, Principal → IC_senior
  • Titles containing: Manager, Team Lead → Manager
  • Titles containing: Director, Head of → Director
  • Titles containing: VP, Vice President, SVP, EVP → VP
  • Titles containing: Chief, C-level abbreviations (CTO, CMO, CRO, CFO), President → C_Level
  • Titles containing: Founder, Co-founder, Owner → Founder

Adjust for company size:

  • At companies <20 employees: inflate seniority one level (a "Manager" has Director-level scope)
  • At companies >5000 employees: deflate seniority one level (a "Director" may have Manager-level autonomy)
Output

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"
}

Step 4: Stakeholder Discovery

Process

For each company in the lead list, identify other relevant people — the buying committee.

Why this matters:

  • Inbound leads are rarely the sole decision-maker
  • Finding the rest of the buying committee early accelerates the deal
  • Multi-threading (engaging multiple people at a company) dramatically improves win rates

Who to find (based on buyer personas from config):

  1. Economic buyer — Person who signs the check. Usually VP+ or C-level in the relevant department.
  2. Champion — Person most likely to push for adoption internally. Usually a senior IC or Director who feels the pain.
  3. Technical evaluator — Person who will assess the product's technical fit. Usually engineering or ops.
  4. End user — Person who will use the product daily. Their buy-in prevents post-sale churn.

Process per company:

  1. Using the buyer personas, determine which roles to search for
  2. Search via configured tool (SixtyFour or Orthogonal, LinkedIn, company-contact-finder)
  3. For each stakeholder found, capture: name, title, seniority, LinkedIn URL, email (if available)
  4. Note the relationship to the inbound lead: same team? Same department? Different function?

Depth control:

  • Deep enrichment (Tier 1-2 leads): Find all 4 stakeholder types. Research each.
  • Standard enrichment (Tier 3 leads): Find economic buyer + champion only.
  • Minimal enrichment (Tier 4 / untiered): Skip stakeholder discovery.
Output

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": ""
}
Stakeholder Prioritization

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


Show full SKILL.md (693 more words)Show less

Step 5: Relationship Check

Process

For each lead AND each discovered stakeholder, check existing systems for prior relationships:

Check 1 — CRM (HubSpot, Salesforce, CSV):

  • Does this person already exist in our system?
  • If yes: what's their current status? (active lead, contacted, nurture, customer, churned)
  • If yes: who owns the relationship?

Check 2 — Outreach history (outreach_log):

  • Have we emailed/messaged this person before?
  • If yes: when, what channel, what was the outcome?
  • Critical: prevent the "we cold-emailed you last week and you ignored us, now you came inbound" collision

Check 3 — Company-level pipeline (companies table or CRM):

  • Is there an active deal with this company?
  • If yes: at what stage? Who's the deal owner?
  • An inbound lead at a company with an active deal is a HUGE signal — flag it prominently

Check 4 — Signal history (signals table):

  • Has this company appeared in any signal scans?
  • If yes: which signals? Were they acted on?

Check 5 — Mutual connections (if data available):

  • Does anyone on our team know someone at this company?
  • If yes: note the warm intro path
Output

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": ""
}

Step 6: Compile & Output

Enriched Lead Record

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": ""
  }
}
Output Formats

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 columnscompany_description, employee_count, industry, stage, hq, tech_stack, recent_newscurrent_title, seniority, department, tenure, linkedin_url, headlinestakeholder_1_name, stakeholder_1_title, stakeholder_1_role, ... (up to 4)in_crm, crm_status, in_pipeline, deal_stage, outreach_history_summaryenrichment_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

markdown
## 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]

Handling Edge Cases

Lead with only an email, nothing else:

  1. Extract domain → look up company
  2. Search "[name] [company]" on LinkedIn/web
  3. If still can't identify: flag as enrichment_failed with reason, recommend manual lookup
  4. Don't waste paid API credits on truly unidentifiable leads — web search first

Same company appears multiple times (multiple inbound leads):

  • Research the company ONCE, apply to all leads
  • Stakeholder discovery runs once per company, not per lead
  • Note the multi-lead signal: "3 people from [Company] came inbound — buying committee forming?"

Lead claims a title that doesn't match LinkedIn:

  • Trust LinkedIn over self-reported form data (people sometimes inflate titles on forms)
  • Note the discrepancy: "Form says 'VP of Engineering', LinkedIn says 'Senior Engineer'"
  • Use the LinkedIn title for qualification purposes

Company recently renamed, merged, or was acquired:

  • If the company domain redirects, follow the redirect
  • Note the corporate action: "[Company] was acquired by [Parent] in [date]"
  • Qualify against the current entity, not the historical one

Person left the company since filling the form:

  • If LinkedIn shows a different company than the form submission, flag it
  • Note: "Lead submitted via [Company] but now at [New Company] as of [date]"
  • Qualify both companies if both are potentially relevant

Enrichment tool rate limits or failures:

  • If primary tool fails, fall back to secondary (web search is always available)
  • If both fail for a specific lead, mark as enrichment_partial and move on
  • Never block the entire batch because one lead failed

Very high volume (100+ leads):

  • Batch company research first (deduplicate companies)
  • Parallelize person research via Task agents (15-20 leads per batch)
  • Skip stakeholder discovery for Tier 4 and untiered leads
  • Provide cost estimate before running paid enrichment tools

Personal email domains:

  • If form had a company field: use it even though email is personal
  • If no company info: attempt LinkedIn search by name
  • Last resort: flag as company_unidentified, enrich person only

Update Protocol

After enrichment is complete, update the source systems:

  1. If CRM is configured: Create/update lead records with enriched data. Don't overwrite existing data — append or fill gaps only.
  2. Flag duplicates: If enrichment reveals a lead already exists in CRM under a different email, flag the duplicate rather than creating a second record.

Tools Required

  • Web search — primary fallback for all research
  • SixtyFour or Orthogonal — company + person lookup (optional, enhances depth)
  • LinkedIn scraper (Apify) — person profile enrichment (optional)
  • Company-contact-finder — stakeholder discovery
  • CRM access — relationship checks (HubSpot, Salesforce, CSV)
  • Read/Write — CSV I/O and config management
  • Task tool — for parallelizing enrichment across large lead batches

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/lead-generation/composites/inbound-lead-enrichment of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Inbound Lead Enrichment

What does Inbound Lead Enrichment do?

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…. Inbound Lead Enrichment is an agent skill from gooseworks-ai/goose-skills. 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.

When should I use Inbound Lead Enrichment?

Inbound Lead Enrichment fits situations like: tasks that involve Lead generation.

How do I install Inbound Lead Enrichment in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a claude-code`. Or copy the skill folder (skills/lead-generation/composites/inbound-lead-enrichment in gooseworks-ai/goose-skills) into .claude/skills/inbound-lead-enrichment in your project. Claude Code loads it when a task matches its description.

How do I install Inbound Lead Enrichment in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a codex`. Or copy the skill folder (skills/lead-generation/composites/inbound-lead-enrichment in gooseworks-ai/goose-skills) into .agents/skills/inbound-lead-enrichment in your project. Codex loads it when a task matches its description.

Can I use Inbound Lead Enrichment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/inbound-lead-enrichment, .gemini/skills/inbound-lead-enrichment, .github/skills/inbound-lead-enrichment and .opencode/skills/inbound-lead-enrichment in your project.

What does Inbound Lead Enrichment need to run?

SKILL.md names no scripts, command-line tools or credentials: Inbound Lead Enrichment is instructions for the agent only.

Does Inbound Lead Enrichment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Inbound Lead Enrichment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Inbound Lead Enrichment use?

Inbound Lead Enrichment is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Inbound Lead Enrichment use?

About 4.6k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Inbound Lead Enrichment?

Skills that share tags, products or a category with Inbound Lead Enrichment: Find Leads (eracle/OpenOutreach, 3.2k stars), Business Contact and Social Links Finder (browser-act/skills, 6.1k stars), Google Maps API Skill (browser-act/skills, 6.1k stars) and Google Maps Reviews API Skill (browser-act/skills, 6.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Inbound Lead Enrichment?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.