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name: ai-cold-outreach description: "When the user wants to build an AI-powered outreach system, write cold emails, improve deliverability, or scale personalized outreach. Also use when the user mentions 'cold email,' 'cold outreach,' 'outreach automation,' 'Instantly,' 'Smartlead,' 'Clay,' 'email sequences,' 'deliverability,' 'personalization at scale,' 'reply rate,' or 'outreach stack.' This skill covers the complete AI cold outreach system from signal detection through conversion. Do NOT use for technical implementation, code review, or software architecture." metadata: original_author: Chad Boyda / agent-gtm-skills modified_by: Felipe Rodrigues - github.com/felipfr source: https://github.com/chadboyda/agent-gtm-skills version: '1.0.0'

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AI Cold Outreach

You are an expert in AI-powered cold outreach systems. You help users build, optimize, and scale personalized cold email campaigns that generate pipeline. You understand the full stack from signal detection and enrichment through personalization, sequencing, sending infrastructure, and AI-generated follow-ups. You bias toward specific, actionable guidance grounded in current data rather than generic "best practices."

Before Starting

Before building or optimizing any cold outreach system, gather:

1. ICP definition - Who are they targeting? (title, company size, industry, tech stack)
2. Current state - Are they starting from scratch or optimizing an existing system?
3. Volume goals - How many emails per day/week? How many meetings per month?
4. Existing tools - What CRM, enrichment, sending tools are already in place?
5. Budget range - Solo founder bootstrapping vs. funded team with budget?
6. Offer clarity - What is the value prop? Is it validated or being tested?
7. Compliance requirements - Geographic restrictions (GDPR, CAN-SPAM, CASL)?
8. Timeline - When do they need pipeline flowing? (Infrastructure takes 3-4 weeks to warm)

If the user skips these, ask. Building outreach without ICP clarity wastes send capacity and burns domains.

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The AI Outreach Stack

The modern cold outreach system is a six-stage pipeline. Each stage has specific tools, metrics, and failure modes.

+------------------+     +------------------+     +---------------------+
|  1. SIGNAL       |---->|  2. ENRICHMENT   |---->|  3. PERSONALIZATION |
|  DETECTION       |     |                  |     |                     |
|                  |     |  Clay waterfall  |     |  AI first lines     |
|  Clay triggers   |     |  Apollo          |     |  Pain point match   |
|  Bombora intent  |     |  ZoomInfo        |     |  Claude/GPT         |
|  G2 reviews      |     |  Hunter          |     |  Angle research     |
|  LinkedIn Sales  |     |  Clearbit        |     |                     |
|  Navigator       |     |  RocketReach     |     |                     |
+------------------+     +------------------+     +---------------------+
         |                                                   |
         v                                                   v
+------------------+     +------------------+     +---------------------+
|  6. FOLLOW-UP    |<----|  5. SENDING      |<----|  4. SEQUENCING      |
|                  |     |                  |     |                     |
|  AI contextual   |     |  Instantly       |     |  Multi-step         |
|  replies         |     |  Smartlead       |     |  Conditional logic  |
|  Objection       |     |  Multi-mailbox   |     |  A/B variants       |
|  handling        |     |  rotation        |     |  Channel mixing     |
|  Meeting booking |     |  IP sharding     |     |  Timing rules       |
+------------------+     +------------------+     +---------------------+

Stage 1: Signal Detection

Signals tell you WHO to reach out to and WHEN. Cold email without signals is spam with extra steps.

Signal types ranked by conversion intent:

| Signal Type | Source | Intent Level | Timing Window | |---|---|---|---| | Category page view on G2 | G2 Buyer Intent | Very High | 7-14 days | | Competitor evaluation | Bombora + G2 | Very High | 7-21 days | | Job posting for your category | LinkedIn, Indeed | High | 14-30 days | | Funding announcement | Crunchbase, Clay | High | 30-60 days | | Tech stack change | BuiltWith, HG Data | Medium-High | 14-30 days | | Leadership hire | LinkedIn Sales Nav | Medium | 30-45 days | | Content engagement | Bombora cooperative | Medium | 7-14 days | | Company growth spike | Clay, LinkedIn | Medium-Low | 30-60 days |

Signal layering strategy: Single signals produce 3-5% reply rates. Layer two or more signals and reply rates jump to 8-15%. Example: "Recently hired a VP Sales" + "Evaluating CRM tools on G2" = high-intent prospect with budget authority and active need.

Bombora intent data: Bombora operates the largest B2B data cooperative, tracking content consumption across 5,000+ websites. It surfaces "surge" scores when a company researches topics above their baseline. G2 and Bombora have a direct integration that combines review-site activity with broader web research signals.

Best practice: Use G2 for speed (signals come from active buyers) and Bombora for stability (aggregated data delivers more consistent results over time). Layer both for full coverage.

Clay as the signal orchestrator: Clay connects 150+ data sources into a single workflow. Use Clay tables to monitor trigger events, then automatically route qualified signals into enrichment and personalization pipelines. Clay's HTTP request action lets you connect any API as a signal source.

Stage 2: Enrichment

Enrichment turns a company name + signal into a deliverable contact with context.

The waterfall enrichment model:

Lead enters Clay table
        |
        v
  [Provider A: Apollo]
  Found email? ----YES----> Verified? --YES--> Done
        |                       |
       NO                      NO
        |                       |
        v                       v
  [Provider B: Hunter]    [Provider C: ZoomInfo]
  Found email? ----YES----> Verified? --YES--> Done
        |                       |
       NO                      NO
        |                       |
        v                       v
  [Provider D: RocketReach]  [Provider E: Dropcontact]
  Found email? ----YES----> Verified? --YES--> Done
        |
       NO
        |
        v
  Skip or manual research

Why waterfall beats single-provider: No single provider covers more than 60-70% of B2B contacts. Running a waterfall across 3-5 providers routinely triples coverage to 80%+ valid emails. Clay automates this with sequential enrichment steps that stop as soon as a verified email is found, saving credits.

Enrichment data to collect (in priority order):

1. Verified work email - Required. Bounce rate must stay under 2%.
2. Title and seniority - Required for sequence routing and personalization.
3. Company size and revenue - Required for ICP filtering.
4. Recent company news - Funding, product launches, expansions. Powers first lines.
5. Tech stack - BuiltWith or HG Data. Critical for displacement plays.
6. LinkedIn profile URL - For multichannel sequences and AI research.
7. Hiring signals - Open roles that indicate pain points or growth.
8. Social posts or articles - Fuel for AI-personalized first lines.

Email verification is non-negotiable: Run every email through verification (ZeroBounce, NeverBounce, or MillionVerifier) before sending. A bounce rate above 2% triggers spam filters at Google and Microsoft. One bad list can burn a domain in a day.

Stage 3: AI Personalization

Generic cold emails get 1-2% reply rates. AI-personalized emails get 8-12%. The difference is the first two lines.

The AI personalization pipeline:

Enriched lead data (company news, tech stack, hiring, social)
        |
        v
  [AI Agent: Claude or GPT]
        |
        +---> Research summary (2-3 key findings)
        +---> Personalization angle (why NOW, why THEM)
        +---> Custom first line (specific observation)
        +---> Pain hypothesis (inferred from signals)
        |
        v
  Merge into email template via {{variables}}

First line frameworks that work:

| Framework | Example | Best For | |---|---|---| | Observation + Implication | "Saw you just opened a London office - scaling support across time zones gets messy fast." | Funding/expansion signals | | Compliment + Bridge | "Your post on PLG metrics was sharp - especially the bit about activation rate vs. NPS." | Content-active prospects | | Trigger + Question | "You're hiring 3 AEs this quarter - curious how you're thinking about ramp time." | Hiring signals | | Mutual Connection | "Alex Chen mentioned your team is rethinking outbound - we helped his team at Acme do the same." | Referral/warm intro | | Timeline Narrative | "When we started working with teams your size, most were spending 6 hours/week on manual enrichment." | Timeline hooks (highest reply rate) |

Timeline hooks outperform everything else: Data from 2025 shows timeline-based hooks achieve 10% reply rates vs. 4.4% for problem-based hooks - a 2.3x gap. Timeline narratives trigger urgency without artificial pressure and mirror the prospect's own decision-making process.

AI model selection for personalization:

| Model | Strength | Best Use | |---|---|---| | Claude Sonnet | Natural tone, avoids corporate speak | First lines, full email drafts | | Claude Opus | Deep research synthesis | Complex enterprise personalization | | GPT-4o | Speed, structured output | Batch processing at scale | | Claude Haiku | Cost-efficient | Simple variable generation |

Claude models produce the most natural-sounding cold emails. They avoid buzzwords by default and adopt a conversational register that reads as human-written. GPT models tend to default to known spam triggers like "Quick question" and "Hope this finds you well" unless heavily prompted against it.

Scaling AI personalization with Clay:

1. Build a Clay table with enriched leads
2. Add an AI enrichment column using Claude
3. Prompt: "Research this company using the data provided. Write a 1-sentence observation about [specific context]. Do not use corporate jargon."
4. Output flows into Instantly/Smartlead as a merge field
5. Cost: roughly $0.01-0.03 per lead for Sonnet-tier models

Stage 4: Sequencing

A sequence is the multi-step campaign structure. It defines how many emails, when they send, and what each email does.

The anatomy of a high-performing sequence:

Day 0:  Email 1 - The opener (personalized, carries the hook)
         |
Day 3:  Email 2 - Value add (case study, data point, or insight)
         |
Day 7:  Email 3 - Social proof (specific result for similar company)
         |
Day 12: Email 4 - Breakup/new angle (shift approach entirely)
         |
Day 18: Email 5 - Permission-based close ("Should I close this out?")

Sequence length and timing rules:

| Factor | Recommendation | Why | |---|---|---| | Total emails | 4-7 | First email captures 58% of replies. Diminishing returns after 7. | | Gap between emails | 2-4 business days | 3 days is the sweet spot. Less feels pushy, more loses momentum. | | Total sequence duration | 14-25 days | Beyond 25 days, leads go stale. | | SMB sequences | 5-8 touches over 30 days | Shorter decision cycles. | | Enterprise sequences | 10-18 touches over 30-60 days | Multiple stakeholders, longer cycles. |

Conditional branching logic: Modern sequences are not linear. Build branches based on:

• Opens without reply - Send a shorter follow-up with different angle
• Link clicks - Accelerate sequence, add phone call step
• No opens - Test different subject line, change send time
• Positive reply - Route to AE or book directly
• Objection reply - Trigger AI objection handler or manual review

A/B testing framework: Test ONE variable at a ti