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Sequence Architecture

Community
kenny589
sequence-architecture

Design multi-step outbound sequences that convert. Timing, channel mix, variant strategy, and escalation logic for cold email campaigns.

Overview

Publisherkenny589
Repositorygtm-flywheel
Skill namesequence-architecture
Stars
71
Forks
26
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kenny589 on GitHub. Read the source before you install it.

Installation

Install the Sequence Architecture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kenny589/gtm-flywheel/tree/main/cold-email/sequence-architecture
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kenny589/gtm-flywheel.git /tmp/gtm-flywheel
mkdir -p .claude/skills
cp -r /tmp/gtm-flywheel/cold-email/sequence-architecture .claude/skills/sequence-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sequence Architecture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sequence Architecture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sequence Architecture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sequence Architecture

When to Use

  • Building a new outbound campaign from scratch
  • Restructuring sequences with poor step-2+ engagement
  • Designing multi-channel cadences (email + LinkedIn + phone)
  • Setting up A/B testing frameworks for sequence optimization

Framework

Sequence Design Principles

  1. Every step earns the next — Step 1's job is to get Step 2 opened. Step 2's job is to get a reply. Never front-load everything into Step 1.
  2. New value every touch — Each step must introduce a new angle, insight, or proof point. "Following up" is not a strategy.
  3. Decay is natural — Open and reply rates drop with each step. Design for this: your best material goes in Steps 1-2, your boldest ask goes in the final step.
  4. Variants multiply learning — Run 2-3 subject/body variants per step. At 200+ sends per variant, you have statistical significance.

The 4-Step Core Sequence

This is the baseline architecture. Adapt timing and angles to your ICP.

StepDayChannelObjectiveAngle Strategy
10EmailOpen + ReplyLead with strongest signal or observation
23-4EmailReplyNew value: resource, benchmark, or case study
37-8EmailReply or ReferralSocial proof or creative ideas angle
414EmailClose or RedirectBreakup ("should I close this out?") or referral ask

Total campaign duration: 14 days Total touches: 4 emails


Extended Sequence (Multi-Channel)

For high-value accounts or enterprise targets, layer in LinkedIn and phone:

StepDayChannelObjective
10EmailSignal-based opening
21LinkedInConnection request with personalized note
33EmailNew value angle
45LinkedInEngage with their content (like/comment)
57EmailCase study or social proof
68Phone"Following up on the email I sent"
710LinkedInDirect message with resource
814EmailBreakup or referral

Total campaign duration: 14 days Total touches: 8 (4 email + 3 LinkedIn + 1 phone)


Variant Strategy

Every email step should have 2-3 variants to enable testing:

Variant TypeWhat You're TestingExample
Subject variantsWhich framing gets opensA: {{company}} outbound vs B: quick question
Opening variantsWhich personalization resonatesA: Signal-based vs B: Problem-based
CTA variantsWhich ask gets repliesA: "Worth a look?" vs B: "Want me to send the playbook?"
Angle variantsWhich value prop convertsA: ROI-focused vs B: Time-savings-focused

Testing rules:

  • Only test ONE variable per variant pair (subject OR body, not both)
  • Minimum 200 sends per variant before drawing conclusions
  • Winner = highest positive reply rate (not total reply rate — ignore "not interested" replies)
  • Rotate winning variants into the "control" and test new challengers

Timing Optimization

FactorRecommendationWhy
Send daysTuesday-ThursdayMonday = inbox overload. Friday = checked out.
Send window8:00 AM - 11:00 AM prospect's timezoneCaught during morning email triage
Step gaps3-4 days between emailsEnough to not feel pushy, close enough to maintain context
Reply windowCheck within 30 min of sendsFast replies to "interested" responses 3x close rate
Match lead ESPEnabledSend from Gmail to Gmail, Outlook to Outlook — improves deliverability

Sequence Settings Checklist

Configure these before launching any campaign:

SettingValueReason
Stop on replyONNever email someone who already responded
Stop on auto-replyOFFAuto-replies (OOO) shouldn't kill the sequence
Text onlyONHTML emails trigger spam filters more often
Link trackingOFFTracking pixels and links hurt deliverability
Open trackingOFFSame — open tracking = invisible pixel = spam risk
Daily send limitPer account limitsRespect your email provider's sending limits
Email gap10-20 minutesTime between individual sends — avoids burst patterns
Random delay5-10 minutesAdds randomness to look human

Escalation Logic

What happens when the standard sequence doesn't convert:

Standard Sequence (14 days)
    ↓ No reply
Wait 30 days
    ↓
Re-engagement Sequence (different angle, 3 steps)
    ↓ No reply
Wait 60 days
    ↓
Trigger-based Re-entry
    (Only re-enter if new signal detected: funding, hiring, tech change)

Never:

  • Re-enroll someone in the same sequence
  • Contact someone who explicitly said "not interested"
  • Send more than 2 sequences to the same person in 6 months without a new signal

Campaign Naming Convention

Use a consistent naming structure so performance data is analyzable:

{Client} - {Segment} - {Angle} - {Version}

Examples:

  • Acme - Enterprise VP Sales - Hiring Signal - v1
  • Acme - SMB Founders - Referral Ceiling - v2
  • Acme - Mid-Market Growth - Tech Stack Change - v1

This makes it trivial to filter analytics by client, segment, or angle.


Architecture Decision Tree

Is the target account high-value (>$50K ACV)?
├── YES → Extended Multi-Channel Sequence (8 touches, 14 days)
│         Layer email + LinkedIn + phone
│         Personalize every touch individually
│
└── NO → Standard 4-Step Email Sequence (4 touches, 14 days)
         Use signal-based personalization at scale
         Rely on variants for optimization

Is this a new persona/segment you haven't tested?
├── YES → Run 3 variants of Step 1 with 200+ sends each
│         Wait for data before building Steps 2-4
│
└── NO → Clone your best-performing sequence
         Swap personalization for new segment
         Keep winning structure intact

Templates

Campaign Launch Checklist

Pre-Launch:
- [ ] ICP and persona defined
- [ ] Lead list built and verified (bounce rate < 3%)
- [ ] Email accounts warmed (2+ weeks, 30+ emails/day)
- [ ] Sequence written (2-3 variants per step)
- [ ] Settings configured (tracking off, text-only, stop on reply)
- [ ] Naming convention applied
- [ ] Test send to yourself and team for QA

Post-Launch (Day 3):
- [ ] Check open rates (target: 60%+)
- [ ] Check bounce rate (must be < 3%)
- [ ] Check spam complaints (must be 0)
- [ ] Review any replies for pattern recognition

Post-Launch (Day 14):
- [ ] Calculate positive reply rate per variant
- [ ] Identify winning variants
- [ ] Document learnings
- [ ] Plan next iteration

Tips

  • Build your sequence backwards — write the breakup email first, then work back to Step 1. This forces you to think about the full arc.
  • The best Step 2 is a reply to Step 1 (literally in the same thread), not a new email. This uses the "thread effect" for higher opens.
  • Don't over-engineer your first sequence. Launch with 4 steps, 2 variants each, and iterate from data. Perfection before launch = wasted time.
  • Keep a "swipe file" of every reply you get — positive and negative. The language prospects use to say "yes" and "no" is gold for future copy.
  • If your open rate drops below 40% mid-campaign, stop sending. You have a deliverability problem, not a copy problem.

Progressive disclosure: load channel-specific templates and industry playbooks only when building sequences for a specific campaign.

Frequently asked questions

What does the Sequence Architecture AI skill do?

Design multi-step outbound sequences that convert. Timing, channel mix, variant strategy, and escalation logic for cold email campaigns.

Why use Sequence Architecture on TypingMind?

Because you install it once and use it with any model. Sequence Architecture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sequence Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kenny589/gtm-flywheel/tree/main/cold-email/sequence-architecture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sequence Architecture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sequence Architecture?

As many as you like. As long as a model supports skills, you can use Sequence Architecture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sequence Architecture AI skill free?

Yes. It is published on GitHub by kenny589 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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