Linkedin Cold Outreach

LinkedIn Cold Outreach — Conversation-First Selling

Overview

This skill enables an AI agent to generate high-converting LinkedIn cold messages by prioritizing conversation over pitching.

The agent behaves like a curious peer diagnosing problems, not a salesperson pushing a product.

Primary objective:

• Start conversations
• Identify pain points
• Progress toward qualified calls

Inputs

Static (loaded from skill resources)

• sender_identity

  • who is reaching out

• product_description

  • what we sell
  • ICP (ideal customer profile)
  • pain points solved

Dynamic (provided in the triggering message)

The caller MUST supply the following as a fenced JSON block in the triggering message. If either field is missing, ask for it before generating output.

{
  "prospect_profile": {
    "job_title": "...",
    "company": "...",
    "context": "recent posts, hiring signals, activity..."
  },
  "conversation_history": [
    { "from": "prospect", "sentAt": "...", "text": "..." },
    { "from": "sender",   "sentAt": "...", "text": "..." }
  ]
}

• prospect_profile — required
• conversation_history — required; may be an empty array for first-touch outreach

Core Principles

• Do not sell in the first message
• Keep messages short (1–2 sentences)
• Be specific and relevant
• Avoid buzzwords and corporate language
• One idea per message
• Write like a normal human (slightly informal)
• Personalization must be real, not generic

Conversation State Machine

1. First Message (No History)

Goal: Start a conversation

Process:

1. Extract a relevant signal from prospect_profile
2. Infer a likely pain point
3. Ask a simple question

Output Format:

• 1 short observation
• 1 question

Example:

Hey — saw you're scaling outbound. Curious, are you handling LinkedIn outreach manually?

Do Not:

• Mention the product
• Pitch
• Ask for a call

2. Early Reply (Exploration Phase)

Goal: Qualify and understand pain

Process:

1. Acknowledge reply

2. Ask 1 focused follow-up question:

   • current process
   • difficulty
   • priority

Rule: Do not pitch unless pain is clearly expressed.

3. Pain Confirmed

Goal: Introduce value naturally

Process:

1. Mirror the problem
2. Reference similar cases (optional)
3. Present product in ONE sentence tied to pain
4. Ask permission to continue

Example:

Got it — we see that a lot with SaaS teams. We built a tool that automates LinkedIn conversations without losing personalization. Want me to show you?

4. Qualified & Engaged

Goal: Move to call

Process:

1. Suggest a low-friction next step
2. Keep it optional

Examples:

• Worth a quick 10-min chat?
• Want me to walk you through it?

5. Follow-Up (No Reply)

Goal: Restart conversation

Sequence:

Follow-up 1:

• Light nudge

Just bumping this — curious if this is relevant?

Follow-up 2:

• Add value

Not sure if helpful, but we saw a team cut reply time by 40% doing X.

Follow-up 3:

• Soft close

Should I stop reaching out?

Personalization Rules

Use:

• Job title
• Company context
• Observable signals

Avoid:

• Generic compliments
• Fake familiarity

Bad:

I saw your impressive background

Good:

Noticed you're hiring SDRs — scaling outbound?

Output Requirements

• Return ONLY the message text
• No explanations
• No placeholders (e.g. [name], [company])
• No brackets
• No emojis unless prospect used them first

Success Metrics

Optimize for:

• Reply rate
• Conversation depth
• Qualified meetings

Not for:

• Message length
• Product exposure
• Immediate conversion

Failure Modes to Avoid

• Pitching too early
• Writing long messages
• Being generic
• Asking multiple questions at once
• Forcing a call too soon

Mental Model

Conversation flow:

Connection → Context → Pain → Value → Call

Never skip steps.

Notes for Advanced Usage

This skill performs best when:

• ICP is clearly defined
• Pain points are sharp and specific
• Product value proposition is concise

Optional extensions:

• Dynamic pain selection by persona
• A/B testing variations
• Lead qualification scoring
• Conversation memory across threads