MarketingCampaign planningintermediate

Product Hunt & Launch Day Campaign Playbook Planner

Execute a systematic 14-day launch runway covering hunter coordination, maker comments, community outreach, and social assets.

#product-hunt #launch-strategy #go-to-market #campaign-planning #community-growth

Compatibility & Specs

Compatible AI Models

ClaudeChatGPT

Last UpdatedApr 1, 2026

Customizable Variables4 parameters

Main Prompt Template4 Variables

153 words • 1142 characters

You are a Growth Marketing Director who has orchestrated multiple #1 Product of the Day and Top 10 Product of the Week launches on Product Hunt. I am preparing to launch [product_name] publicly. Product Overview & Core Value Proposition: [product_description] Target Launch Date / Runway: [launch_timeline] Existing Audience & Community Channels: [existing_assets] Deliver the complete Launch Day Operating Playbook: 1. 14-Day Pre-Launch Runway Schedule: Day-by-day checklist covering thumbnail design, animated GIF teasers, Hunter outreach vs. self-hunting, and landing page load-testing. 2. High-Converting Product Hunt Listing Assets: (A) Catchy 60-character Tagline, (B) First Maker Comment (personal founding story, problems solved, and exclusive launch offer), (C) First 5 Q&A seed comments. 3. Hacker News 'Show HN' Execution Plan: Un-hyped, transparent, technical submission title and plain-text launch essay adhering to strict HN community norms. 4. Multi-Channel Launch Day Roster (Twitter/X, LinkedIn, Discord): Formatted launch announcements adapted to each platform's native tone without triggering algorithmic spam penalties.

Tip:Variables marked in purple brackets can be tailored below.

Customize Prompt

Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.

Parameters (4)* Indicates required field

Product Name*[product_name]

Name of the product launching

Product Description*[product_description]

What it does and what makes it special

Launch Timeline*[launch_timeline]

Days until launch

Existing Audience Assets[existing_assets]

Newsletter subscribers, Twitter followers, Discord members

4 parameters active

Live Customized OutputLive Preview

204 words

You are a Growth Marketing Director who has orchestrated multiple #1 Product of the Day and Top 10 Product of the Week launches on Product Hunt. I am preparing to launch VectorDesk (AI-Powered Customer Support Triage) publicly. Product Overview & Core Value Proposition: A modern, privacy-first customer support copilot that drafts hyper-accurate ticket responses grounded directly in your company's Git repos, Linear tickets, and Notion knowledge base without training on customer data. Target Launch Date / Runway: Launching in 14 days on a Tuesday 12:01 AM PST Existing Audience & Community Channels: 1,800 newsletter subscribers, 3,200 Twitter followers, 250 active Discord beta users Deliver the complete Launch Day Operating Playbook: 1. 14-Day Pre-Launch Runway Schedule: Day-by-day checklist covering thumbnail design, animated GIF teasers, Hunter outreach vs. self-hunting, and landing page load-testing. 2. High-Converting Product Hunt Listing Assets: (A) Catchy 60-character Tagline, (B) First Maker Comment (personal founding story, problems solved, and exclusive launch offer), (C) First 5 Q&A seed comments. 3. Hacker News 'Show HN' Execution Plan: Un-hyped, transparent, technical submission title and plain-text launch essay adhering to strict HN community norms. 4. Multi-Channel Launch Day Roster (Twitter/X, LinkedIn, Discord): Formatted launch announcements adapted to each platform's native tone without triggering algorithmic spam penalties.

Launch in:

ChatGPT Claude Gemini

How to Use This Prompt

Follow this 3-step workflow to extract high-signal responses from any compatible AI model.

1. Tailor the Parameters

Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.

2. Send to AI Model

Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.

3. Review and Iterate

Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.

Prompt Variables & Parameters

Reference breakdown of every dynamic variable embedded in this prompt template.

Placeholder	Parameter Name	Type	Status	Description & Guidance
[product_name]	Product Name	text	Required	Name of the product launchingDefault: VectorDesk (AI-Powered Customer Support Triage)
[product_description]	Product Description	textarea	Required	What it does and what makes it specialDefault: A modern, privacy-first customer support copilot that drafts hyper-accurate ticket responses grounded directly in your company's Git repos, Linear tickets, and Notion knowledge base without training on customer data.
[launch_timeline]	Launch Timeline	text	Required	Days until launchDefault: Launching in 14 days on a Tuesday 12:01 AM PST
[existing_assets]	Existing Audience Assets	text	Optional	Newsletter subscribers, Twitter followers, Discord membersDefault: 1,800 newsletter subscribers, 3,200 Twitter followers, 250 active Discord beta users

Example Execution & Benchmark Output

Sample input arguments and the verified AI response demonstrating expected quality and formatting.

Sample VariablesInputs

[product_name]

VectorDesk (AI-Powered Customer Support Triage)

[product_description]

A modern, privacy-first customer support copilot that drafts hyper-accurate ticket responses grounded directly in your company's Git repos, Linear tickets, and Notion knowledge base without training on customer data.

[launch_timeline]

Launching in 14 days on a Tuesday 12:01 AM PST

[existing_assets]

1,800 newsletter subscribers, 3,200 Twitter followers, 250 active Discord beta users

Expected AI ResponseVerified Result

### 1. High-Converting Product Hunt Assets - **Tagline (Strict 60 chars):** *"AI customer support copilot grounded in your engineering docs"* (59 chars) - **Maker Comment Template (First 2 Minutes):** *"Hey Product Hunt! 👋 I'm Alex, co-founder of VectorDesk. Two years ago while scaling our previous startup, our support inbox was flooded with technical bug inquiries. Support reps spent 40% of their day DMing engineers on Slack just to answer basic integration questions. Existing AI bots hallucinated answers or gave bland canned replies. We built VectorDesk to give support agents a real-time copilot grounded directly in technical source material: GitHub PRs, Linear tickets, and Notion wikis—with zero customer data used for model training. Today, we're giving the PH community 50% off for your first 3 months with code `PHLAUNCH`. Would love your raw feedback on our ticket draft speed!"* ### 2. Hacker News 'Show HN' Playbook - **Title Rule:** Zero emojis, zero buzzwords, zero marketing fluff. - **Title:** `Show HN: VectorDesk – Customer support copilot grounded in engineering docs` - **Opening:** *"Hi HN, we built VectorDesk because existing LLM support bots don't understand code or internal issue trackers..."*

Best Use Cases

Scenarios and roles where this prompt produces maximum leverage.

Founders and indie hackers preparing to launch on Product Hunt and Hacker News

Marketing teams coordinating cross-platform public product announcements

Startups looking to maximize initial user signups on launch day

Tips for Best Results

Techniques to elevate response fidelity

•Explicitly define your Ideal Customer Profile (ICP), their acute pain points, and current legacy alternatives.
•Provide strict negative constraints: list corporate clichés, overused jargon, or vague promises the model must avoid.
•Ask the model to critique its own copy from the perspective of a cynical, time-pressed prospect before finalizing.

Common Mistakes to Avoid

Frequent failure modes and anti-patterns

•Failing to define a singular, clear Call-to-Action (CTA), resulting in unfocused and diluted messaging.
•Accepting high-level marketing buzzwords that sound impressive but say nothing concrete to users.
•Not fact-checking competitive claims, legal guarantees, or pricing specifications generated by the model.

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