Clay Automation logo

Clay Automation

Organization
LeadMagic
clay-automation

Build production-grade Clay enrichment workflows — table architecture, waterfall configuration, Claygent AI research, Sculptor table building, CRM push with clay_status properties, credit optimization. Use when building Clay tables, configuring enrichment waterfalls, setting up Claygent, or automating GTM workflows in Clay. Triggers on: "Clay", "Clay workflow", "Clay table", "Claygent", "Sculptor", "Clay enrichment", "Clay waterfall", "Clay automation", or any request about building workflows in Clay.

Overview

PublisherLeadMagic
Repositorygtm-skills
Skill nameclay-automation
Stars
72
Forks
18
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeadMagic on GitHub. Read the source before you install it.

Installation

Install the Clay Automation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeadMagic/gtm-skills/tree/main/skills/automation/clay-automation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LeadMagic/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/automation/clay-automation .claude/skills/clay-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clay Automation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Clay Automation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Clay Automation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Clay Automation

Overview

Clay is the orchestration layer where enrichment, scoring, and routing converge. Used correctly, it's a GTM multiplier. Used incorrectly, it's a credit-burning machine producing data nobody trusts.

This skill covers when and how to roll out Clay automation — data quality, table separation, rollout phases, and n8n handoff.

Playbook index: references/automation-playbook-index.md — all 38 automation + tool + gtm-ops playbooks.

Automation strategy (before tool config): Jen Igartua (Go Nimbly) — data before AI, maturity levels 0–4, human+machine division. Canonical → references/gtm-automation-expert-playbook.md (Pattern 30). Outbound copy/infra → Eric Nowoslawski / Pat Spielmann / Justin Michael — not this skill.

Tool implementation (table columns, LeadMagic waterfalls, loops) lives in tools/:

  • clay-toolkit (tools/clay-toolkit) — tables and waterfalls
  • clay-loops-toolkit (tools/clay-loops-toolkit) — signal loops

When to Use

  • "Build a Clay enrichment workflow"
  • "Set up a Clay table for our prospecting"
  • "Configure a waterfall in Clay"
  • "Use Claygent for research"
  • "Push enriched data from Clay to HubSpot"
  • "Optimize our Clay credit usage"
  • "Design a Clay automation pipeline"

Authoritative Foundations

Clay workflow design follows patterns from DAMA-DMBOK data-quality dimensions, Ziellab (3 separate waterfalls: company, email, phone), and GTME Pulse (10 production templates tested at $5M-$100M ARR companies).

The core principle: Clay is a routing engine, not a CRM. Enriched data lives in your CRM; Clay processes it en route.

  • Eric Nowoslawski — Crawl Walk Run (Growth Engine X). Roll out Clay automation in phases: Crawl — manual campaigns for 5–10 companies (no AI); Walk — system prompt with business context + manual examples, review first 50 outputs; Run — automate in Clay → Smartlead/SEP with Supabase for block lists. Default campaign type: Creative Ideas (3 constrained capabilities per prospect). Playbook → references/eric-nowoslawski-outbound.md.

Prerequisites

  • Clay account (Pro plan or above for waterfall features and conditional columns)
  • API keys for enrichment providers (Apollo, ZoomInfo, PDL, etc.)
  • CRM connected (HubSpot, Salesforce, or Attio)
  • ICP defined and documented

Step-by-Step Process

Phase 1: Table Architecture

Rule: separate company and person tables.

Company table: one row per domain. Enriches firmographics, tech stack, company-level qualification once.

Person table: one row per contact. References company data via domain lookup. Enriches email, phone, LinkedIn, role-level qualification.

This separation prevents credit waste from re-enriching the same company data across every contact row.

Phase 2: Waterfall Configuration

For each data field, configure a conditional waterfall:

FieldPrimaryFallback 1Fallback 2
EmailLeadMagic Email FinderApolloHunter
PhoneApolloCognismContactOut
Company dataClay nativeApollo CompanyClearbit

Set conditions: each fallback only fires when the previous step returns empty or error. Use Clay's conditional logic: "Only run if [previous column] is blank."

Phase 3: Claygent Configuration

Claygent is AI-powered web research. Configure prompts explicitly:

Good prompt: "Find the work email for [name] at [company]. Search the company's team page, LinkedIn profile, and press releases. Return the email AND the source URL. Do NOT guess or construct emails from patterns. If no verified source, return empty."

Bad prompt (don't use): "Find me their email."

Critical rules:

  • Always require source URL citation
  • Explicitly prohibit pattern-guessing
  • Set credit cap per Claygent call (5-10 credits)
  • Use only for the 10-15% that structured providers miss

Phase 4: CRM Push

Push enriched data to CRM with a clay_status property:

clay_statusMeaningAction
pendingIn enrichmentHold — not ready
enrichedEnrichment completeReady for verification
verifiedVerified and safeCan enter sequences
exportedPushed to CRMDone, archive in Clay

Only contacts with clay_status = verified enter sequences.

Phase 5: Credit Optimization

  1. Qualify first, enrich deep later. Run ICP filters before expensive contact enrichment. Cuts costs 30-40%.

  2. Credit caps per row. Set max 5-6 credits per row. If a contact is that hard to find, they're probably not a good fit.

  3. Native integrations over HTTP API. Clay's native provider integrations are rate-limited and credit-billed correctly. HTTP API calls bypass Clay's cache and often double-charge.

  4. Batch overnight. Claygent is 15-40s per row. Run large Claygent batches during off-hours.

Output Format

Clay workflow document with table architecture diagram, provider waterfall configuration, Claygent prompt templates, CRM push rules, credit budget, and maintenance schedule.

Quality Check

  • Company and person tables separated
  • Waterfalls configured with conditional fallback logic
  • Claygent prompts explicitly prohibit guessing
  • CRM push uses clay_status property gating
  • Credit caps set per row
  • Test batch (50 rows) validated before scaling

Common Pitfalls

  1. One giant table. Combining company and person data wastes credits and makes re-enrichment impossible. Separate always.

  2. Enriching before filtering. Running $0.15-0.40/contact enrichment on non-ICP records wastes budget. Filter on cheap data first.

  3. Claygent guessing emails. Without explicit "do not guess" instructions, Claygent constructs pattern-based emails that bounce at 40-60% rates.

  4. Two-way CRM sync. Clay-to-CRM should be one direction. CRM-to-Clay sync creates data conflicts. Push only.

  5. Clay as permanent storage. Clay is a workspace. Push to CRM, archive or delete rows in Clay. Old rows decay just like anywhere else.

  6. No credit caps. Without caps, a single row can chew through 15+ credits. Cap at 5-6 per row.

Execution Artifacts

  • references/framework-notes.md — named frameworks, citation anchors, and operating assumptions
  • templates/output-template.md — copy-paste deliverable structure for the user
  • scripts/check-output.py — local checklist validator for required sections This skill includes lightweight artifacts the agent can load on demand:
  • references/gtm-automation-expert-playbook.md — Jen Igartua RevOps automation strategy (Pattern 30)
  • references/eric-nowoslawski-outbound.md — Crawl Walk Run, Creative Ideas, GEX stack (Eric Nowoslawski) Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.

Related Skills

  • waterfall-enrichment: Deep waterfall architecture
  • lead-enrichment: Enrichment execution patterns
  • list-building: List building workflows in Clay
  • clay-toolkit: tools/clay-toolkit — table blueprints, LeadMagic waterfalls
  • clay-loops-toolkit: tools/clay-loops-toolkit — signal loops and routing
  • ai-prompts-toolkit: Claygent and LLM prompt library
  • n8n-automation: n8n as Clay export for complex cases
  • crm-integration: CRM configuration for Clay data

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Clay Automation AI skill do?

Build production-grade Clay enrichment workflows — table architecture, waterfall configuration, Claygent AI research, Sculptor table building, CRM push with clay_status properties, credit optimization. Use when building Clay tables, configuring enrichment waterfalls, setting up Claygent, or automating GTM workflows in Clay. Triggers on: "Clay", "Clay workflow", "Clay table", "Claygent", "Sculptor", "Clay enrichment", "Clay waterfall", "Clay automation", or any request about building workflows in Clay.

Why use Clay Automation on TypingMind?

Because you install it once and use it with any model. Clay Automation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Clay Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/automation/clay-automation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Clay Automation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Clay Automation?

As many as you like. As long as a model supports skills, you can use Clay Automation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Clay Automation AI skill free?

Yes. It is published on GitHub by LeadMagic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇