| 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"} |
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:
- ICP definition - Who are they targeting? (title, company size, industry, tech stack)
- Current state - Are they starting from scratch or optimizing an existing system?
- Volume goals - How many emails per day/week? How many meetings per month?
- Existing tools - What CRM, enrichment, sending tools are already in place?
- Budget range - Solo founder bootstrapping vs. funded team with budget?
- Offer clarity - What is the value prop? Is it validated or being tested?
- Compliance requirements - Geographic restrictions (GDPR, CAN-SPAM, CASL)?
- 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.
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):
- Verified work email - Required. Bounce rate must stay under 2%.
- Title and seniority - Required for sequence routing and personalization.
- Company size and revenue - Required for ICP filtering.
- Recent company news - Funding, product launches, expansions. Powers first lines.
- Tech stack - BuiltWith or HG Data. Critical for displacement plays.
- LinkedIn profile URL - For multichannel sequences and AI research.
- Hiring signals - Open roles that indicate pain points or growth.
- 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:
- Build a Clay table with enriched leads
- Add an AI enrichment column using Claude
- Prompt: "Research this company using the data provided. Write a 1-sentence observation about [specific context]. Do not use corporate jargon."
- Output flows into Instantly/Smartlead as a merge field
- 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 time across minimum 200 sends per variant:
| Priority | Variable | Impact on Reply Rate |
|---|
| 1 | Subject line | 20-40% swing in open rate |
| 2 | First line / hook | 2-3x reply rate difference |
| 3 | CTA style | 1.5-2x reply rate difference |
| 4 | Email length | Moderate impact |
| 5 | Send time | Marginal impact |
Stage 5: Sending Infrastructure
Infrastructure is where most outreach systems break. Perfect copy with bad deliverability lands in spam.
Domain and mailbox architecture:
Primary Domain: yourcompany.com
(NEVER use for cold outreach)
Secondary Domains (for outreach only):
yourcompany-team.com --> mailbox1@, mailbox2@
tryyourcompany.com --> mailbox1@, mailbox2@
getyourcompany.com --> mailbox1@, mailbox2@
yourcompanyhq.com --> mailbox1@, mailbox2@
Formula:
Daily volume target / 150 = domains needed (round up)
Add 30-50% for rotation reserve
Example: 600 emails/day
600 / 150 = 4 domains minimum
+ 50% reserve = 6 domains total
x 2 mailboxes each = 12 mailboxes
Infrastructure sizing guide:
| Daily Volume | Domains Needed | Mailboxes | Monthly Domain Cost |
|---|
| 100-200 | 2-3 | 4-6 | $20-30 |
| 300-500 | 3-5 | 6-10 | $30-50 |
| 500-1,000 | 5-8 | 10-16 | $50-80 |
| 1,000-2,000 | 8-15 | 16-30 | $80-150 |
| 2,000+ | 15+ | 30+ | $150+ |
Per-mailbox sending limits:
| Type | Daily Limit | Notes |
|---|
| Warmup emails | 15-20/day | Run for 14-21 days before cold sends |
| Cold emails | 25-30/day | Never exceed 40 |
| Combined total | 40-50/day | Stay under provider thresholds |
Domain warmup protocol:
| Week | Daily Volume/Mailbox | Activity |
|---|
| Week 1 | 10-15 | Warmup only, no cold sends |
| Week 2 | 20-30 | Warmup + 5-10 cold sends |
| Week 3 | 30-40 | Warmup + 15-20 cold sends |
| Week 4 | 40-50 | Full cold sending capacity |
Authentication setup checklist (do this on Day 1):
Authenticated senders are 2.7x more likely to reach the inbox vs. unauthenticated.
DMARC rollout sequence:
- Week 1-2:
p=none with reporting (rua=mailto:dmarc@yourdomain.com)
- Week 3-4: Review reports, fix any alignment issues
- Week 5-6:
p=quarantine (soft enforcement)
- Week 7+:
p=reject (full enforcement)
Never jump straight to p=reject before inventorying all legitimate senders.
Sending platform comparison: Instantly vs. Smartlead
| Feature | Instantly | Smartlead |
|---|
| Best for | Solo founders, small teams | Agencies, high-volume senders |
| Pricing (entry) | $37/mo | $33/mo |
| Pricing (scale) | $97-358/mo | $94-174/mo |
| Email accounts | Unlimited (Growth+) | Unlimited (all plans) |
| Built-in lead database | Yes (SuperSearch, 450M+) | No (import only) |
| Warmup network | 4.2M+ accounts | Smaller network |
| AI reply agent | Yes (responds in <5 min) | Limited |
| Deliverability approach | IP sharding + rotation (SISR) | Human-mimicking variable volume |
| Sending behavior | Exact daily volume | Variable (sends 22 when set to 25) |
| API and webhook support | Good | Excellent (API-first) |
| White-label | Limited | Full white-label |
| CRM integration | Built-in basic CRM | Via integrations |
| Clay integration | Native | Native |
| Inbox rotation | Automatic | Automatic |
| Campaign analytics | Detailed dashboards | Detailed dashboards |
| Multi-channel | Email + LinkedIn (beta) | Email focused |
Decision framework:
Need built-in lead database?
YES --> Instantly
NO --> Continue
Running an agency or white-labeling?
YES --> Smartlead
NO --> Continue
Need AI auto-replies?
YES --> Instantly
NO --> Continue
Sending 1,000+/day and need API control?
YES --> Smartlead
NO --> Continue
Want simplest setup and UI?
YES --> Instantly
NO --> Smartlead
Stage 6: AI-Powered Follow-Up
Most replies are not "Yes, let's meet." They are questions, objections, or soft interest. AI follow-up handles these at scale.
Reply categories and handling:
| Reply Type | % of Replies | AI Action |
|---|
| Positive interest | 25-35% | Book meeting link, confirm time |
| Question about offer | 20-30% | Answer with specifics, re-CTA |
| Objection (timing) | 15-20% | Acknowledge, offer future follow-up |
| Objection (budget) | 5-10% | Share ROI data, offer smaller entry |
| Referral to colleague | 10-15% | Thank, ask for intro or direct email |
| Not interested | 10-15% | Thank, remove from sequence |
| Auto-reply/OOO | 5-10% | Pause, re-send after return date |
AI reply handling setup:
- Classify reply intent with AI (positive, question, objection, referral, not interested)
- Route positive replies to a human or booking link immediately
- Generate contextual responses for questions and objections
- Set a human review flag for any edge cases
- Auto-remove "not interested" from all sequences (compliance requirement)
Instantly's AI Reply Agent handles this natively and responds in under 5 minutes. Smartlead users typically build this with Clay + webhook integrations.
The 3-Line Cold Email Framework
The highest-performing cold emails in 2026 follow a simple structure: three lines, under 80 words, zero fluff.
Line 1 (PAIN): A specific observation about their situation.
Derived from signal data + AI research.
NOT "Are you struggling with X?" (everyone sends this).
Line 2 (PROOF): One sentence of credibility.
A specific result for a similar company.
NOT "We're the leading platform for..."
Line 3 (CTA): A low-friction ask.
NOT "Book 30 minutes on my calendar."
YES "Worth a quick look?" or "Open to hearing more?"
Example (good):
Noticed you just raised your Series B and are hiring 4 AEs - ramping that many reps
without standardized outbound playbooks usually means 2-3 months of wasted pipeline.
We helped Acme's team cut AE ramp from 90 to 45 days after their Series B.
Worth a 10-minute look at how?
Example (bad):
Hi [Name], I hope this email finds you well. I'm reaching out because I noticed your
company is growing. We're the leading sales enablement platform trusted by 500+
companies. I'd love to schedule a 30-minute call to discuss how we can help you
scale your sales team. Would Tuesday at 2pm work?
Why the bad example fails:
- "Hope this finds you well" - spam trigger, zero value
- Generic observation - "growing" applies to everyone
- Self-centered proof - "leading platform" is unverifiable
- High-friction CTA - 30 minutes is a big ask from a stranger
- Too long - 75 words of fluff before any value
Cold email anatomy rules:
| Element | Rule | Why |
|---|
| Subject line | 2-5 words, lowercase, no punctuation | Looks like an internal email |
| Preview text | First 40 chars of body visible in inbox | Make the hook visible |
| Word count | 50-125 words | Under 50 feels incomplete, over 125 loses attention |
| Paragraphs | 1-2 sentences each | Mobile-friendly whitespace |
| Links | Zero in first email | Links trigger spam filters |
| Images | Zero in first email | Images trigger spam filters |
| Attachments | Zero in first email | Attachments trigger spam filters |
| Signature | Name + title + company only | Minimal, no banners or social icons |
| CTA | One per email | Multiple CTAs reduce response rate |
| Personalization | First 1-2 lines | Generic everything else is fine if the hook lands |
For benchmarks, deliverability playbook, week-by-week build, cost analysis, failure modes, and advanced tactics read references/benchmarks-deliverability-tactics.md.
Examples
- User says: "Build a cold email sequence for our SaaS" → Result: Agent gathers ICP and volume, recommends 3-line email framework (observation + relevance + CTA), suggests Instantly + Clay stack, and outputs a 5–7 touch sequence with subject lines and spacing.
- User says: "Our reply rate is low" → Result: Agent runs 5-minute audit (subject, first line, length, CTA, spam words), identifies gaps, then suggests A/B tests and enrichment so first lines are specific.
- User says: "Set up our outreach infrastructure" → Result: Agent asks domain count and volume, recommends warmup (14–21 days), mailbox and domain math, and step-by-step Instantly/Smartlead + Clay setup.
Troubleshooting
- Low reply rates → Cause: Generic first lines, no signal-based targeting, or weak CTA. Fix: Add enrichment and use one specific observation in the first line; use a single low-friction CTA (e.g. reply or short call).
- Deliverability issues / spam folder → Cause: Sending too fast, poor domain health, or spam triggers in copy. Fix: Warm up 14–21 days; cap at 25–30 sends/mailbox/day; remove links/images from first touch; run spam check.
- Meetings don’t show up → Cause: CTA is too big (e.g. "book 30 min") or sequence stops too early. Fix: Use lower-friction CTA first (reply, short call); extend to 5–7 touches with 3–5 day spacing.
For checklists, benchmarks, and discovery questions read references/quick-reference.md when you need detailed reference.
Related Skills
- ai-sdr - Building AI-powered SDR agents that automate the full outreach workflow
- lead-enrichment - Deep dive on waterfall enrichment, data providers, and verification
- video-outreach - Adding personalized video to cold sequences for higher engagement
- sales-motion-design - Designing the complete sales motion that outreach feeds into
- gtm-engineering - Technical infrastructure for outreach systems, APIs, and data pipelines
- solo-founder-gtm - Lean outreach playbooks for founders doing their own outbound
- positioning-icp - Nailing the ICP and positioning before building outreach
- content-to-pipeline - Using content as a warm-up channel before cold outreach
- social-selling - LinkedIn-native selling that complements email outreach
