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Cold Outreach Sequence

Community
BrianRWagner
cold-outreach-sequence

Build personalized cold outreach sequences for LinkedIn and email. Use when someone needs to reach prospects, warm up cold leads, or build a systematic outreach engine. Covers research, connection requests, follow-ups, and conversion.

Overview

PublisherBrianRWagner
Repositoryai-marketing-claude-code-skills
Skill namecold-outreach-sequence
Stars
449
Forks
103
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Cold Outreach Sequence 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/BrianRWagner/ai-marketing-claude-code-skills/tree/main/cold-outreach-sequence
  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/BrianRWagner/ai-marketing-claude-code-skills.git /tmp/ai-marketing-claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-claude-code-skills/cold-outreach-sequence .claude/skills/cold-outreach-sequence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cold Outreach Sequence 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 Cold Outreach Sequence 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 Cold Outreach Sequence 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.

Cold Outreach Sequence

Here's what I've learned about cold outreach: the word "cold" is the problem.

Spray-and-pray templates don't work. 10 minutes of research + a specific reference = not cold anymore. This skill builds the second kind.


Mode

Detect from context or ask: "One message, full sequence, or full outreach system?"

ModeWhat you getBest for
quick1 connection request + 1 follow-up for a single prospectTesting an angle, one-off outreach
standardFull 4-touch sequence for a single prospectActive pipeline, individual targets
deepMulti-prospect sequence system + A/B variants + tracking frameworkLaunching an outreach campaign

Default: standard — use quick if they give you one name and say "draft something." Use deep if they're building a repeatable outreach engine.


Context Loading Gates

Before writing any message, collect:

  • Prospect name and company — full name, company, role/title
  • Research signals — run tool calls first (see below); do not write without them
  • Sender positioning — what does the sender do, for whom, with what result? (Use positioning-basics output if available)
  • Platform — LinkedIn DM, email, or both?
  • Batch size — how many prospects? (determines tier assignment)

Research tool calls — run before writing:

web_search('[Company] [Founder/Name] news 2026')
web_search('[Company] funding recent')
web_search('[Person name] [Company] LinkedIn')

Personalization constraint: Do not write a Tier 1 message without a named specific signal from research. If search yields 0 signals, default to Tier 3 and say so explicitly.


Phase 1: Research & Signal Assessment

For each prospect, document findings before drafting:

Signal types (ranked by message strength):

  1. Recent news event (funding, launch, hire, press) → strongest signal
  2. Recent LinkedIn post activity → strong signal
  3. Company stage/growth data → medium signal
  4. Role + industry awareness only → weak signal (Tier 3)

Personalization tier assignment:

Research ResultTierApproach
Named signal (news + post + context)Tier 1Fully custom, reference signal in every message
Company info + role contextTier 2Template + personalized opener
No signals foundTier 3Volume template, minimal customization

Phase 2: Sequence Generation

Connection Request (LinkedIn) — 300 chars max

Formula: [Specific observation from research] + [Simple reason to connect]

Rules:

  • No pitching
  • Prove you did research (name the signal)
  • One sentence, conversational
  • Never "I'd love to pick your brain"

By signal type:

Recent funding: "Congrats on the Series A — the [investor] backing is a smart signal. Would love to connect."

Recent post: "Your post on [specific topic] resonated — been thinking the same thing. Happy to connect."

News/launch: "Saw the [product] launch — [specific detail] is smart positioning. Would love to connect."

First Message (After Accept — Wait 24-48 Hours)

Formula: [Thanks] + [Bridge to relevance] + [Light value] + [Soft question]

Template:

Thanks for connecting. I work with [ICP description] on [specific outcome].

Curious — is [relevant function] something you own directly at [Company], 
or is that still founder-led?

Happy to share what I'm seeing work at similar-stage companies either way.

Follow-Up #1 (Day 7)

Formula: [Light nudge] + [New signal or angle] + [Easy out]

Constraint: Do NOT write "following up" with nothing new. Add one new piece:

  • A relevant article or trend
  • A related insight you recently had
  • A connection to something they posted

Template:

Bumping this up — came across [specific article/trend/insight] and 
thought of your situation at [Company].

[One sentence on why it's relevant to them.]

Happy to share more if useful. If not, no worries.

Follow-Up #2 (Day 14)

Shift to email if LinkedIn hasn't converted, or try a different angle.

Subject line options:

  • "[Company]'s [function] as you scale"
  • "Saw your [post/news] — quick thought"
  • "Question about [specific thing they're doing]"

Email structure:

[1-line hook tied to their specific situation]

[2-3 sentences: why you're reaching out + one proof point]

[Soft CTA — 1 sentence]

Break-Up Message (Day 21)

I'll assume timing isn't right — totally get it. 

If [relevant pain point] becomes a priority down the road, happy to reconnect. 
Best of luck with [specific thing they're working on based on research].

Post-break-up action: Add to 6-month re-engagement list with a resurface date.


Phase 3: Self-Critique Pass (REQUIRED)

After generating the full sequence, evaluate:

  • Does every message reference the specific signal from research, or are they generic?
  • Is the connection request under 300 characters?
  • Does the first message ask a question (invite dialogue) rather than pitch?
  • Does follow-up #1 add something genuinely new, or is it just "following up"?
  • Does the break-up message reference something specific about their situation?
  • Did I correctly assign the personalization tier, or am I over-personalizing a Tier 3 prospect?

Flag any issue: "The first message doesn't include a soft question — it reads as a pitch. Revised to invite dialogue."


Pipeline Tracking Table

Always output a tracking table for the batch:

markdown
| Prospect | Company | Platform | Tier | Sent Date | Response | Stage | Next Action | Resurface Date |
|---|---|---|---|---|---|---|---|---|
| [Name] | [Co] | LinkedIn | 1 | [date] | — | Connection sent | Wait 24-48h | — |
| [Name] | [Co] | Email | 2 | [date] | — | First email sent | Follow-up Day 7 | — |

Iteration Protocol

After each response (or non-response), ask:

  • Did the connection request get accepted? If low acceptance rate → revise the observation line
  • Did the first message get a reply? If no → was the question soft enough, or did it feel like a pitch?
  • Did follow-ups get ignored? If yes → try a different angle or acknowledge the silence directly

Output Structure

markdown
## Outreach Sequence: [Prospect Name] — [Date]

### Research Summary
- Signal type: [news / post / company info / none]
- Signal found: "[Specific detail]"
- Personalization tier: [1/2/3]
- Source: [URL or platform]

### Sequence

**Connection Request (LinkedIn):**
[Text — max 300 chars]

**First Message (Day 1-2 after accept):**
[Text]

**Follow-Up #1 (Day 7):**
[Text]

**Follow-Up #2 (Day 14):**
Platform: [LinkedIn / Email]
Subject: [if email]
[Text]

**Break-Up (Day 21):**
[Text]

### Pipeline Entry
| Prospect | Company | Platform | Tier | Stage | Next Action | Resurface Date |
|---|---|---|---|---|---|---|
| [Name] | [Co] | [Platform] | [Tier] | Connection sent | Wait 24-48h | — |

### Self-Critique Notes
[Any issues flagged + revisions made]

Skill by Brian Wagner | AI Marketing Architect | brianrwagner.com

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cold Outreach Sequence AI skill do?

Build personalized cold outreach sequences for LinkedIn and email. Use when someone needs to reach prospects, warm up cold leads, or build a systematic outreach engine. Covers research, connection requests, follow-ups, and conversion.

Why use Cold Outreach Sequence on TypingMind?

Because you install it once and use it with any model. Cold Outreach Sequence 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 Cold Outreach Sequence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BrianRWagner/ai-marketing-claude-code-skills/tree/main/cold-outreach-sequence. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cold Outreach Sequence?

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 Cold Outreach Sequence?

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

Is the Cold Outreach Sequence AI skill free?

It is published on GitHub by BrianRWagner. Check the repository for licensing terms. 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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