Clay automation skill

Build production-grade Clay enrichment workflows — table architecture, waterfall configuration, Claygent AI research, Sculptor table building, CRM push with clay_status properties, credit optimization.

by LeadMagic·MIT license·★ 62 Stars on the repo·GitHub ↗

Use now

Inside this skill

SKILL.md · the author's own words
# 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.

Word-for-word excerpt, not a result. Read the full SKILL.md ↓

Files of Clay automation

LeadMagic/main1 file shown
SKILL.md
Show the full text206 lines

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:

Field Primary Fallback 1 Fallback 2
Email LeadMagic Email Finder Apollo Hunter
Phone Apollo Cognism ContactOut
Company data Clay native Apollo Company Clearbit

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_status Meaning Action
pending In enrichment Hold — not ready
enriched Enrichment complete Ready for verification
verified Verified and safe Can enter sequences
exported Pushed to CRM Done, 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.
  • 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
1---
2name: clay-automation
3description: >-
4 Build production-grade Clay enrichment workflows — table architecture, waterfall
5 configuration, Claygent AI research, Sculptor table building, CRM push with
6 clay_status properties, credit optimization. Use when building Clay tables,
7 configuring enrichment waterfalls, setting up Claygent, or automating GTM
8 workflows in Clay. Triggers on: "Clay", "Clay workflow", "Clay table", "Claygent",
9 "Sculptor", "Clay enrichment", "Clay waterfall", "Clay automation", or any
10 request about building workflows in Clay.
11license: MIT
12compatibility: Claude Code, Codex, GitHub Copilot, Cursor, Gemini CLI, OpenCode, Goose, Hermes, Windsurf, Zed
13metadata:
14 version: "1.0.0"
15 author: LeadMagic
16 category: automation
17 tags: [clay, automation, enrichment, waterfall, workflows]
18 related_skills: [clay-toolkit, clay-loops-toolkit, ai-prompts-toolkit, waterfall-enrichment, lead-enrichment, list-building, n8n-automation]
19 frameworks: [DAMA-DMBOK Data Quality Dimensions, Ziellab 3-Waterfall Architecture, GTME Pulse Clay Templates, Eric Nowoslawski Crawl Walk Run, Eric Nowoslawski Creative Ideas Campaign, Eric Nowoslawski GEX Agency Stack, Jen Igartua Go Nimbly RevOps automation maturity]
20---
21 
22# Clay Automation
23 
24## Overview
25 
26Clay is the orchestration layer where enrichment, scoring, and routing converge.
27Used correctly, it's a GTM multiplier. Used incorrectly, it's a credit-burning
28machine producing data nobody trusts.
29 
30This skill covers **when and how to roll out** Clay automation — data quality,
31table separation, rollout phases, and n8n handoff.
32 
33**Playbook index:** `references/automation-playbook-index.md` — all 38 automation + tool + gtm-ops playbooks.
34 
35**Automation strategy (before tool config):** Jen Igartua (Go Nimbly) — data before AI,
36maturity levels 0–4, human+machine division. Canonical → `references/gtm-automation-expert-playbook.md`
37(Pattern 30). Outbound copy/infra → Eric Nowoslawski / Pat Spielmann / Justin Michael — not this skill.
38 
39**Tool implementation**
40(table columns, LeadMagic waterfalls, loops) lives in `tools/`:
41- `clay-toolkit` (`tools/clay-toolkit`) — tables and waterfalls
42- `clay-loops-toolkit` (`tools/clay-loops-toolkit`) — signal loops
43 
44## When to Use
45 
46- "Build a Clay enrichment workflow"
47- "Set up a Clay table for our prospecting"
48- "Configure a waterfall in Clay"
49- "Use Claygent for research"
50- "Push enriched data from Clay to HubSpot"
51- "Optimize our Clay credit usage"
52- "Design a Clay automation pipeline"
53 
54## Authoritative Foundations
55 
56Clay workflow design follows patterns from DAMA-DMBOK data-quality dimensions,
57Ziellab (3 separate waterfalls: company, email, phone), and GTME Pulse
58(10 production templates tested at $5M-$100M ARR companies).
59 
60The core principle: Clay is a routing engine, not a CRM. Enriched data
61lives in your CRM; Clay processes it en route.
62 
63- **Eric Nowoslawski — Crawl Walk Run (Growth Engine X).** Roll out Clay
64 automation in phases: **Crawl** — manual campaigns for 5–10 companies (no AI);
65 **Walk** — system prompt with business context + manual examples, review first
66 50 outputs; **Run** — automate in Clay → Smartlead/SEP with Supabase for block
67 lists. Default campaign type: **Creative Ideas** (3 constrained capabilities per
68 prospect). Playbook → `references/eric-nowoslawski-outbound.md`.
69 
70## Prerequisites
71 
72- Clay account (Pro plan or above for waterfall features and conditional columns)
73- API keys for enrichment providers (Apollo, ZoomInfo, PDL, etc.)
74- CRM connected (HubSpot, Salesforce, or Attio)
75- ICP defined and documented
76 
77## Step-by-Step Process
78 
79### Phase 1: Table Architecture
80 
81**Rule: separate company and person tables.**
82 
83Company table: one row per domain. Enriches firmographics, tech stack,
84company-level qualification once.
85 
86Person table: one row per contact. References company data via domain
87lookup. Enriches email, phone, LinkedIn, role-level qualification.
88 
89This separation prevents credit waste from re-enriching the same company
90data across every contact row.
91 
92### Phase 2: Waterfall Configuration
93 
94For each data field, configure a conditional waterfall:
95 
96| Field | Primary | Fallback 1 | Fallback 2 |
97|---|---|---|---|
98| Email | LeadMagic Email Finder | Apollo | Hunter |
99| Phone | Apollo | Cognism | ContactOut |
100| Company data | Clay native | Apollo Company | Clearbit |
101 
102Set conditions: each fallback only fires when the previous step returns
103empty or error. Use Clay's conditional logic: "Only run if [previous
104column] is blank."
105 
106### Phase 3: Claygent Configuration
107 
108Claygent is AI-powered web research. Configure prompts explicitly:
109 
110**Good prompt:** "Find the work email for [name] at [company]. Search
111the company's team page, LinkedIn profile, and press releases. Return
112the email AND the source URL. Do NOT guess or construct emails from
113patterns. If no verified source, return empty."
114 
115**Bad prompt (don't use):** "Find me their email."
116 
117Critical rules:
118- Always require source URL citation
119- Explicitly prohibit pattern-guessing
120- Set credit cap per Claygent call (5-10 credits)
121- Use only for the 10-15% that structured providers miss
122 
123### Phase 4: CRM Push
124 
125Push enriched data to CRM with a clay_status property:
126 
127| clay_status | Meaning | Action |
128|---|---|---|
129| pending | In enrichment | Hold — not ready |
130| enriched | Enrichment complete | Ready for verification |
131| verified | Verified and safe | Can enter sequences |
132| exported | Pushed to CRM | Done, archive in Clay |
133 
134Only contacts with clay_status = verified enter sequences.
135 
136### Phase 5: Credit Optimization
137 
1381. **Qualify first, enrich deep later.** Run ICP filters before expensive
139 contact enrichment. Cuts costs 30-40%.
140 
1412. **Credit caps per row.** Set max 5-6 credits per row. If a contact
142 is that hard to find, they're probably not a good fit.
143 
1443. **Native integrations over HTTP API.** Clay's native provider integrations
145 are rate-limited and credit-billed correctly. HTTP API calls bypass Clay's
146 cache and often double-charge.
147 
1484. **Batch overnight.** Claygent is 15-40s per row. Run large Claygent
149 batches during off-hours.
150 
151## Output Format
152 
153Clay workflow document with table architecture diagram, provider waterfall
154configuration, Claygent prompt templates, CRM push rules, credit budget,
155and maintenance schedule.
156 
157## Quality Check
158 
159- [ ] Company and person tables separated
160- [ ] Waterfalls configured with conditional fallback logic
161- [ ] Claygent prompts explicitly prohibit guessing
162- [ ] CRM push uses clay_status property gating
163- [ ] Credit caps set per row
164- [ ] Test batch (50 rows) validated before scaling
165 
166## Common Pitfalls
167 
1681. **One giant table.** Combining company and person data wastes credits
169 and makes re-enrichment impossible. Separate always.
170 
1712. **Enriching before filtering.** Running $0.15-0.40/contact enrichment on
172 non-ICP records wastes budget. Filter on cheap data first.
173 
1743. **Claygent guessing emails.** Without explicit "do not guess" instructions,
175 Claygent constructs pattern-based emails that bounce at 40-60% rates.
176 
1774. **Two-way CRM sync.** Clay-to-CRM should be one direction. CRM-to-Clay
178 sync creates data conflicts. Push only.
179 
1805. **Clay as permanent storage.** Clay is a workspace. Push to CRM, archive
181 or delete rows in Clay. Old rows decay just like anywhere else.
182 
1836. **No credit caps.** Without caps, a single row can chew through 15+ credits.
184 Cap at 5-6 per row.
185 
186## Execution Artifacts
187 
188- `references/framework-notes.md` — named frameworks, citation anchors, and operating assumptions
189- `templates/output-template.md` — copy-paste deliverable structure for the user
190- `scripts/check-output.py` — local checklist validator for required sections
191This skill includes lightweight artifacts the agent can load on demand:
192- `references/gtm-automation-expert-playbook.md` — Jen Igartua RevOps automation strategy (Pattern 30)
193- `references/eric-nowoslawski-outbound.md` — Crawl Walk Run, Creative Ideas, GEX stack (Eric Nowoslawski)
194Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.
195 
196## Related Skills
197 
198- **waterfall-enrichment**: Deep waterfall architecture
199- **lead-enrichment**: Enrichment execution patterns
200- **list-building**: List building workflows in Clay
201- **clay-toolkit**: `tools/clay-toolkit` — table blueprints, LeadMagic waterfalls
202- **clay-loops-toolkit**: `tools/clay-loops-toolkit` — signal loops and routing
203- **ai-prompts-toolkit**: Claygent and LLM prompt library
204- **n8n-automation**: n8n as Clay export for complex cases
205- **crm-integration**: CRM configuration for Clay data
206 

Discussion

Alternatives

Browser Automation SkillWeb browser automation with AI-optimized snapshots for claude-flow agentsCoding · MITDeepevalDeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).Business & ops · Apache-2.0Dev browserBrowser automation with persistent named pages via the dev-browser CLI. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, log into sites, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", "open the browser", or any browser interaction request.Business & ops · MITTurn into appTurn visible project context, a proven thread, skill, or workflow into a runnable Agent-Native app with simple buttons, visible agent steps, preview, and deployment handoff. Use when a user invokes `/turn-into-app` or asks to make a workflow into an app, including from Claude or ChatGPT on the web, including when the source is a spreadsheet link or upload.Business & ops · MIT