Agent skill

Gtm Enrichment Deep

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

AI-agent-powered lead enrichment using Sixtyfour as primary source.

MITAuto-check passedMarketing & SEO

Install Gtm Enrichment Deep

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill gtm-enrichment-deep -a claude-code

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

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills gtm-enrichment-deep --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/capabilities/gtm-enrichment-deep .claude/skills/gtm-enrichment-deep && 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
gtm-enrichment-deep
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.2k tokens
SKILL.md length
466 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

AI-agent-powered lead enrichment using Sixtyfour as primary source.

  • Works in 5 steps: Extract Domain → Run Sixtyfour Enrichment (parallel) → Fallback — Apollo Person Match… → …
  • Tasks that involve Go-to-market strategy
  • SKILL.md covers Setup, Input, Workflow and Output Format, plus 4 more sections
  • Calls curl, python3 and npx; reaches linkedin.com and api.gooseworks.ai; needs GOOSEWORKS_API_KEY

What it does

Gtm Enrichment Deep is an agent skill from gooseworks-ai/goose-skills. AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.

Its SKILL.md is about 2.2k 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 Go-to-market strategy and GraphQL. 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 Go-to-market strategy
  • Tasks that involve GraphQL

Example prompts

  • “/gtm-enrichment-deep”

Requirements

  • Python 3
  • Node.js
  • A credential in GOOSEWORKS_API_KEY

Workflow steps

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

  1. Extract Domain
  2. Run Sixtyfour Enrichment (parallel)
  3. Fallback — Apollo Person Match (conditional)
  4. Fallback — Apollo Organization Enrich (conditional)
  5. Compile Results

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

    Shell commands in SKILL.md call:

    • curl
    • python3
    • npx

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • linkedin.com
    • api.gooseworks.ai

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GOOSEWORKS_API_KEY

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

Context cost

Gtm Enrichment Deep loads about 2.2k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 466 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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). 466 words, ~2,179 tokens.

Download SKILL.mdSave it as .claude/skills/gtm-enrichment-deep/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
gtm-enrichment-deep
description
AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
source
orthogonal

GTM Enrichment — Deep (Sixtyfour AI Agent)

Setup

Read your credentials from ~/.gooseworks/credentials.json:

bash
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login

All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"

Enrich a lead from an email address (+ optional name) using Sixtyfour's AI agents as the primary enrichment source. Returns person data, company data, funding history, and AI/B2B classification.

Cost: ~$0.20-$0.22 per lead Latency: ~30-60s (Sixtyfour AI agents browse the web)

Input

Required:

  • email — the lead's email address (e.g., jane@acme.com)

Optional:

  • name — full name if known (improves match rate)

Workflow

Step 1: Extract Domain

Extract the domain from the email address. Example: jane@acme.com -> domain: acme.com

Step 2: Run Sixtyfour Enrichment (parallel)

Fire both calls simultaneously. These are the primary data sources.

Enrich Lead ($0.10):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/enrich-lead"}'
  "lead_info": {
    "email": "{email}",
    "first_name": "{first_name_if_known}",
    "last_name": "{last_name_if_known}",
    "company": "{company_name_if_known}",
    "domain": "{domain}"
  },
  "struct": {
    "full_name": "Full legal name of this person",
    "first_name": "First name",
    "last_name": "Last name",
    "title": "Current job title at their company",
    "linkedin_url": "LinkedIn profile URL (full URL starting with https://linkedin.com/in/)",
    "city": "City where the person is located",
    "state": "State or region where the person is located",
    "country": "Country where the person is located"
  }
}'

Enrich Company ($0.10):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/enrich-company"}'
  "target_company": {
    "domain": "{domain}"
  },
  "struct": {
    "company_name": "Official company name",
    "description": "One-paragraph description of what the company does",
    "linkedin_url": "LinkedIn company page URL (full URL starting with https://linkedin.com/company/)",
    "hq_city": "Headquarters city",
    "hq_state": "Headquarters state or region",
    "hq_country": "Headquarters country",
    "employee_count": "Approximate number of employees (number only)",
    "founded_year": "Year the company was founded (number only)",
    "total_funding_amount_usd": "Total funding raised in USD (number only, no $ sign)",
    "latest_funding_date": "Date of most recent funding round (YYYY-MM-DD format)",
    "latest_funding_stage": "Stage of most recent funding round (e.g., Series A, Series B, Seed)",
    "latest_funding_amount_usd": "Amount raised in most recent round in USD (number only)",
    "is_ai_company": "true or false - does this company build or primarily use AI/ML technology?",
    "ai_evidence": "Brief explanation of why this is or is not an AI company",
    "is_b2b_saas": "true or false - is this a B2B SaaS company?",
    "b2b_evidence": "Brief explanation of why this is or is not B2B SaaS"
  }
}'

Record the status, latency, and any errors for both calls.

Step 3: Fallback — Apollo Person Match (conditional)

ONLY run if Sixtyfour /enrich-lead did NOT return a LinkedIn URL.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"apollo","path":"/api/v1/people/match"}'
  "email": "{email}",
  "reveal_personal_emails": true
}'

Cost: $0.01. Extract linkedin_url, and also grab name, title, organization as cross-reference data.

Step 4: Fallback — Apollo Organization Enrich (conditional)

ONLY run if Sixtyfour /enrich-company did NOT return funding data (total_funding_amount_usd is null/empty).

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"apollo","path":"/api/v1/organizations/enrich","query":{"domain":"{domain}"}}'

Cost: $0.01. Extract funding events, total funding, latest funding stage, and latest funding amount.

Step 5: Compile Results

Merge all data into the output format below. Apply these rules:

  1. Sixtyfour is primary — use its data first for all fields
  2. Apollo is fallback — only used to fill gaps Sixtyfour missed
  3. Source tracking — for each field, note whether it came from sixtyfour or apollo
  4. Confidence:
    • high — Sixtyfour returned the field directly
    • medium — Apollo fallback provided the field
    • low — field was inferred or partially matched
Show full SKILL.md (189 more words)Show less

Output Format

Present the results as a JSON code block:

json
{
  "person": {
    "full_name": "string",
    "title": "string",
    "linkedin_url": "string",
    "location": {"city": "string", "state": "string", "country": "string"},
    "email_verified": "unknown",
    "confidence": "high | medium | low",
    "source": "sixtyfour | apollo"
  },
  "company": {
    "name": "string",
    "domain": "string",
    "linkedin_url": "string",
    "description": "string",
    "geo": {"city": "string", "state": "string", "country": "string"},
    "employee_count": "number | null",
    "founded_year": "number | null",
    "funding": {
      "total_amount": "number | null",
      "total_amount_printed": "string | null",
      "latest_round_date": "string | null",
      "latest_round_stage": "string | null",
      "latest_round_amount": "number | null",
      "rounds": [],
      "confidence": "high | medium | low"
    },
    "classification": {
      "is_ai": {"value": true, "confidence": "high | medium | low", "evidence": ["string"]},
      "is_b2b_saas": {"value": true, "confidence": "high | medium | low", "evidence": ["string"]}
    },
    "buying_signals": {
      "has_enterprise_plan": null,
      "has_self_serve": null,
      "hiring_enterprise_reps": null,
      "website_traffic_rank": null,
      "github_stars": null,
      "tech_stack": null
    },
    "confidence": "high | medium | low",
    "source": "sixtyfour | apollo | merged"
  },
  "meta": {
    "total_cost": "$0.XX",
    "api_calls": [],
    "phases_run": [1, 2],
    "enrichment_timestamp": "ISO datetime"
  }
}

Error Visibility

Track EVERY API call in the meta.api_calls array:

json
{
  "api": "sixtyfour",
  "endpoint": "/enrich-lead",
  "status": "success | partial | error",
  "cost": "$0.10",
  "latency_ms": 35000,
  "fields_returned": ["full_name", "title", "linkedin_url"],
  "fields_missing": ["city"],
  "error": null
}

If an API call fails, returns empty data, or times out, include it in the api_calls array with status='error' and a clear error message. Never silently skip failures.

Cost Tracking

Sum all API call costs and report in meta.total_cost:

  • Sixtyfour /enrich-lead: $0.10
  • Sixtyfour /enrich-company: $0.10
  • Apollo /api/v1/people/match: $0.01 (only if used)
  • Apollo /api/v1/organizations/enrich: $0.01 (only if used)

Example

Input: jane@acme.com

Expected flow:

  1. Extract domain: acme.com
  2. Fire Sixtyfour /enrich-lead and /enrich-company in parallel
  3. Check if LinkedIn URL returned — if not, call Apollo /people/match
  4. Check if funding data returned — if not, call Apollo /organizations/enrich
  5. Compile and output JSON with all fields, error visibility, and cost

Tips

  • Sixtyfour takes 30-60s per call — be patient, do NOT timeout early
  • If Sixtyfour returns partial data, still use what it returned and fill gaps with Apollo
  • AI/B2B classification comes from Sixtyfour's web research — it reads the company website
  • The struct field in Sixtyfour tells the AI agent exactly what to research — modify fields there if you need different data points

© 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/capabilities/gtm-enrichment-deep 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.

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Gtm Enrichment Deep compared with similar skills
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Revenue Centric Designheliocosta-dev/revenue-centric-design740—~1.6kAutomated safety check: PassCustom licence
Startup Designferdinandobons/startup-skill1.2k—~8.1kAutomated safety check: PassMIT
Jaredrhod Marketingjaredrhod/ai-marketing-skills282—~584Automated safety check: PassCC-BY-SA-4.0

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Categories

Questions about Gtm Enrichment Deep

What does Gtm Enrichment Deep do?

AI-agent-powered lead enrichment using Sixtyfour as primary source. Gtm Enrichment Deep is an agent skill from gooseworks-ai/goose-skills. AI-agent-powered lead enrichment using Sixtyfour as primary source.

When should I use Gtm Enrichment Deep?

Gtm Enrichment Deep fits situations like: tasks that involve Go-to-market strategy; tasks that involve GraphQL.

How do I install Gtm Enrichment Deep in Claude Code?

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

How do I install Gtm Enrichment Deep in Codex?

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

Can I use Gtm Enrichment Deep 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 gtm-enrichment-deep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gtm-enrichment-deep, .gemini/skills/gtm-enrichment-deep, .github/skills/gtm-enrichment-deep and .opencode/skills/gtm-enrichment-deep in your project.

What does Gtm Enrichment Deep need to run?

Going by SKILL.md and its folder, Gtm Enrichment Deep needs the command-line tools its instructions call (curl, python3 and npx) and credentials named GOOSEWORKS_API_KEY. Our summary lists: Python 3; Node.js; A credential in GOOSEWORKS_API_KEY.

Does Gtm Enrichment Deep access the network?

SKILL.md names 2 domains. In commands or code: linkedin.com and api.gooseworks.ai; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Gtm Enrichment Deep 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 Gtm Enrichment Deep use?

Gtm Enrichment Deep 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 Gtm Enrichment Deep use?

About 2.2k tokens (SKILL.md is roughly 8.7k 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 Gtm Enrichment Deep?

Skills that share tags, products or a category with Gtm Enrichment Deep: Provider Builder (Othmane-Khadri/YALC-the-GTM-operating-system, 318 stars), Marketing Plan (Nexus-JPF/note-companion, 870 stars), Revenue Centric Design (heliocosta-dev/revenue-centric-design, 740 stars) and Startup Design (ferdinandobons/startup-skill, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gtm Enrichment Deep?

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.