Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/clay-core-workflow-b/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill clay-core-workflow-b. Copyright stays with the author (MIT).

Clay Core Workflow B: Claygent AI Research & Personalization

Overview

Complements the enrichment pipeline (clay-core-workflow-a) with AI-powered research and personalization. Uses Claygent (Clay's built-in AI research agent powered by GPT-4) to scrape websites, extract insights, and generate personalized outreach copy for each prospect. 30% of Clay customers use Claygent daily, generating 500K+ research tasks per day.

Prerequisites

• Completed clay-core-workflow-a with enriched table
• Clay Pro plan or higher (Claygent requires Pro+)
• Understanding of prompt engineering basics

Instructions

Step 1: Add a Claygent Research Column

In your Clay table with enriched leads:

1. Click + Add Column > Use AI (Claygent)
2. Choose model: Claygent Neon (best for data extraction and formatting)
3. Write your research prompt referencing table columns:

Research {{Company Name}} ({{domain}}) and find:
1. Their most recent funding round (amount, date, investors)
2. Any recent product launches or major announcements from the last 6 months
3. Their primary competitors

Return results as structured data. If information is not found, return "Not found" for that field.

1. Enable Auto-run on new rows

Step 2: Configure Multi-Output Claygent (Neon Model)

Claygent Neon can extract multiple data points into separate columns from a single run:

Research the company at {{domain}} and extract:

Output 1 (Recent News): The most notable company news from the last 90 days. One sentence.
Output 2 (Tech Stack): List the main technologies they use (check job postings, BuiltWith, Wappalyzer data).
Output 3 (Pain Points): Based on their Glassdoor reviews and recent job postings, identify likely operational pain points.
Output 4 (Competitor): Name their primary competitor.

Map each output to a separate column for downstream use in personalization.

Step 3: Build a Personalized Email Opener Column

Add an AI column (not Claygent -- use the faster AI model for text generation):

You are a sales copywriter. Write a personalized 2-sentence email opener for {{first_name}} at {{Company Name}}.

Context about the prospect:
- Title: {{Job Title}}
- Company size: {{Employee Count}} employees
- Industry: {{Industry}}
- Recent news: {{Recent News}}
- Tech stack: {{Tech Stack}}

Rules:
- Reference one specific fact about their company (not generic)
- Do NOT use "I noticed" or "I came across" (overused)
- Keep it under 40 words
- Sound human, not AI-generated
- End with a natural transition to your value prop

Step 4: Quality-Check AI Output Before Campaign Launch

Before using AI-generated copy in outreach:

// src/workflows/qa-clay-output.ts
interface ClayRow {
  email: string;
  company_name: string;
  personalized_opener: string;
  icp_score: number;
  recent_news: string;
}

function qaCheck(row: ClayRow): { pass: boolean; issues: string[] } {
  const issues: string[] = [];

  // Check opener quality
  if (!row.personalized_opener || row.personalized_opener.length < 20) {
    issues.push('Opener too short or empty');
  }
  if (row.personalized_opener?.includes('{{')) {
    issues.push('Unresolved template variable in opener');
  }
  if (/I noticed|I came across|I saw that/i.test(row.personalized_opener || '')) {
    issues.push('Opener uses banned phrases');
  }

  // Check data completeness
  if (!row.email) issues.push('Missing email');
  if (row.recent_news === 'Not found') issues.push('No research data found');
  if (row.icp_score < 50) issues.push('Low ICP score');

  return { pass: issues.length === 0, issues };
}

Step 5: Export Campaign-Ready Data

Configure an HTTP API column to push qualified, personalized leads to your outreach tool:

{
  "method": "POST",
  "url": "https://api.instantly.ai/api/v1/lead/add",
  "headers": {
    "Content-Type": "application/json",
    "Authorization": "Bearer {{Instantly API Key}}"
  },
  "body": {
    "campaign_id": "your-campaign-id",
    "email": "{{Work Email}}",
    "first_name": "{{first_name}}",
    "last_name": "{{last_name}}",
    "company_name": "{{Company Name}}",
    "personalization": "{{personalized_opener}}",
    "custom_variables": {
      "recent_news": "{{Recent News}}",
      "tech_stack": "{{Tech Stack}}"
    }
  }
}

Set conditional run: ICP Score >= 70 AND ISNOTEMPTY(Work Email) AND ISNOTEMPTY(personalized_opener)

Step 6: Claygent Navigator for Dynamic Websites

For sites that require interaction (filtering, clicking, scrolling):

1. Add a Claygent column with Navigator mode enabled
2. Navigator can click buttons, fill search forms, and extract data from dynamic pages

Navigate to {{domain}}/pricing and extract:
1. Number of pricing tiers
2. Starting price
3. Whether they offer a free tier
4. Enterprise pricing model (contact sales vs. listed)

If the pricing page requires interaction (e.g., toggle annual/monthly), switch to annual pricing first.

Error Handling

Output

Produce a campaign-preparation manifest with the approved source rows, qualification policy, research/personalization version, quality checks, suppressed records, downstream destination, and owner approval. Research and generated content are evidence to review, not permission to contact a person or to make claims that the source does not support.

Examples

Run Claygent only for a small staging subset that has cleared the ICP and email conditions, sample the resulting openers for accuracy and prohibited claims, and send no messages. If an opener misidentifies a company or includes sensitive data, suppress that row, adjust the prompt/mapping, and retest before release.

Resources

• Clay University -- Claygent AI Web Scraper
• Clay -- 11 AI Prompts for Prospect Research
• Claygent Product Page

Next Steps

For common errors, see clay-common-errors.