Find Leads
eracle/OpenOutreach
Find qualified B2B leads with OpenOutreach — run openoutreach find N [emails], read the CSV it prints on stdout, and hand the rows to whatever sends.
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…
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichment --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "inbound-lead-enrichment" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichment into .claude/skills/inbound-lead-enrichment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inbound-lead-enrichment", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichmentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichment --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/lead-generation/composites/inbound-lead-enrichment .agents/skills/inbound-lead-enrichment && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "inbound-lead-enrichment" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichment into .agents/skills/inbound-lead-enrichment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inbound-lead-enrichment", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichment --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/lead-generation/composites/inbound-lead-enrichment .cursor/skills/inbound-lead-enrichment && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "inbound-lead-enrichment" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichment into .cursor/skills/inbound-lead-enrichment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inbound-lead-enrichment", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/lead-generation/composites/inbound-lead-enrichment--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichment --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/lead-generation/composites/inbound-lead-enrichment .gemini/skills/inbound-lead-enrichment && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "inbound-lead-enrichment" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichment into .gemini/skills/inbound-lead-enrichment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inbound-lead-enrichment", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichmentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/lead-generation/composites/inbound-lead-enrichment .github/skills/inbound-lead-enrichment && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "inbound-lead-enrichment" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichment into .github/skills/inbound-lead-enrichment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inbound-lead-enrichment", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill inbound-lead-enrichment -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills inbound-lead-enrichment --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/lead-generation/composites/inbound-lead-enrichment .opencode/skills/inbound-lead-enrichment && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "inbound-lead-enrichment" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/composites/inbound-lead-enrichment into .opencode/skills/inbound-lead-enrichment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inbound-lead-enrichment", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
inbound-lead-enrichmentFills 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. 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.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,740 words, ~4,578 tokens.
.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.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 recordsOn 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:
© 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
SKILL.md and 1 other file in skills/lead-generation/composites/inbound-lead-enrichment of gooseworks-ai/goose-skills.
Open the folder on GitHubat commit c650c6d
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.
Inbound Lead Enrichment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Inbound Lead Enrichment this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Find Leadseracle/OpenOutreach | 3.2k | — | ~4.6k | Automated safety check: Pass | GPL-3.0 | |
| Business Contact and Social Links Finderbrowser-act/skills | 6.1k | 1 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Google Maps API Skillbrowser-act/skills | 6.1k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Google Maps Reviews API Skillbrowser-act/skills | 6.1k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Google Maps Leadsgmapsscraper/google-maps-agent-skills | 132 | — | ~1.2k | Automated safety check: Pass | MIT |
eracle/OpenOutreach
Find qualified B2B leads with OpenOutreach — run openoutreach find N [emails], read the CSV it prints on stdout, and hand the rows to whatever sends.
browser-act/skills
Finds a company's official website and social profiles from its name, or collects social links from a website URL, using BrowserAct templates run by a Python script.
browser-act/skills
This skill helps users automatically scrape business data from Google Maps using the BrowserAct Google Maps API.
browser-act/skills
This skill is designed to help users automatically extract reviews from Google Maps via the Google Maps Reviews API.
gmapsscraper/google-maps-agent-skills
Generate qualified B2B leads from Google Maps. An agent skill from gmapsscraper/google-maps-agent-skills.
explorium-ai/gtm-skills
Prospecting skill for Claude Code and Codex: build a targeted list of B2B prospects or businesses from a natural-language ICP brief using real-time company and contact data.
gooseworks-ai/goose-skills
Scrape and search Reddit posts using Apify. An agent skill from gooseworks-ai/goose-skills.
gooseworks-ai/goose-skills
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent.
gooseworks-ai/goose-skills
Replace an existing video's opening with a supplied clip or free kinetic text hook while retaining and verifying every original body frame, audio, captions and ending.
gooseworks-ai/goose-skills
Scrape blog posts via RSS feeds (free, no API key) with Apify fallback for JS-heavy sites.
gooseworks-ai/goose-skills
Find leads by scraping engagers from a competitor's top LinkedIn posts.
gooseworks-ai/goose-skills
Assemble a ChatGPT chat-reveal video ad from a thread + timeline JSON — one continuous Playwright recording of a ChatGPT mobile chat (user types with the iOS keyboard up → taps send → keyboard…
Categories
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.
Inbound Lead Enrichment fits situations like: tasks that involve Lead generation.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Inbound Lead Enrichment is instructions for the agent only.
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.
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.
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.
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.
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.
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.