AI Cold Outreach workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
AI Cold Outreach workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages packages/skills-catalog/skills/(gtm)/ai-cold-outreach from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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."
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, The AI Outreach Stack, The 3-Line Cold Email Framework.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Use when the request clearly matches the imported source intent: 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....
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Use when copied upstream references, examples, or scripts materially improve the answer.
Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
references/benchmarks-deliverability-tactics.md
Starts with the smallest copied file that materially changes execution
Supporting context
references/quick-reference.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: 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?
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.
Examples
Example 1: Ask for the upstream workflow directly
Use @ai-cold-outreach to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @ai-cold-outreach against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @ai-cold-outreach for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @ai-cold-outreach using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: 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.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(gtm)/ai-cold-outreach, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Imported Troubleshooting Notes
Imported: 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
@accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
@ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
@ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.
@ai-seo - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/benchmarks-deliverability-tactics.md
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
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.
List-Unsubscribe header added (required by Google, Yahoo, Microsoft)
MX records configured properly
Reverse DNS (PTR record) set up
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.
Imported: 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.
