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Product Hunt Playbook

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Gingiris-1031
product-hunt-playbook

It's 11pm before your launch. Your PH page is ready but you have no idea what actually moves the ranking algorithm. Upvotes? Comments? Time-of-day? This is the hour-by-hour operations manual from someone who's coached 30+ #1 finishes. What's inside: • Ranking algorithm deep-dive (it's not just upvotes — weighted factors decoded) • Launch day minute-by-minute checklist (PST timing, first 3 hours are critical) • Voter mobilization strategies (ethical, anti-spam-safe approaches) • Asset preparation templates (gallery, tagline ≤60 chars, maker comments) • Post-launch 72h momentum plan (convert PH traffic → retained users) Built from: 30+ PH #1 daily wins, 10+ weekly top 3, 2 monthly top 5. 3,000-5,000+ upvotes per launch. Triggers: "Product Hunt" | "PH launch" | "Product Hunt #1" | "PH ranking" | "Product Hunt strategy" | "PH upvotes" | "launch day" | "PH打榜" | "Product Hunt攻略" | "PH ranking algorithm"

Overview

PublisherGingiris-1031
Repositorygingiris-skills
Skill nameproduct-hunt-playbook
Stars
88
Forks
6
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

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

  • Open source

    Published by Gingiris-1031 on GitHub. Read the source before you install it.

Installation

Install the Product Hunt Playbook 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/Gingiris-1031/gingiris-skills/tree/main/skills/product-hunt-playbook
  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/Gingiris-1031/gingiris-skills.git /tmp/gingiris-skills
mkdir -p .claude/skills
cp -r /tmp/gingiris-skills/skills/product-hunt-playbook .claude/skills/product-hunt-playbook
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Hunt Playbook 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 Product Hunt Playbook 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 Product Hunt Playbook 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.

⚠️ 2C 产品慎用 Product Hunt

本 skill 默认 dev / maker / B2B 受众。2C 消费品 / 教育 / 应用 / 游戏：Product Hunt 受众是 maker/科技人，与普通消费者错配、ROI 极低——最多发一次拿个 badge。请把精力换到 垂类社区（Reddit / 小红书 / 知乎 / Naver 카페）+ 短视频（TikTok / 抖音 / YouTube）+ 垂直 KOL，并用"免费产品当钩子"做转化。完整 2C 渠道数据库（各国 MAU + 公开来源）+ 三路 Launch 分叉见 → gingiris-seo-geo/references/2c-adaptation.md（及 gingiris-launch）。

📦 Install

bash
clawhub install product-hunt-playbook

What you get after installing:

  • 24-hour battle plan with hour-by-hour tactics for launch day
  • Ranking algorithm deep-dive — weighted factors decoded (it's not just upvotes)
  • Post-launch 72h momentum plan to convert PH traffic into retained users

Product Hunt Launch Playbook

🌍 Language / 语言: 中文 | English | 日本語 | 한국어

30x Daily #1 | 3x Weekly #1 | 1x Monthly #1

What You Get

  • 📅 24-hour battle plan — hour-by-hour tactics for launch day
  • 🎯 Ranking algorithm insights — how PH ranking actually works
  • 👥 Community mobilization — activate your network without violating rules
  • 💬 Maker comments strategy — engaging with voters effectively
  • 📊 Post-launch momentum — converting PH traffic to users

📚 Free Resources

GitHub Playbook (Free):

bash
clawhub install gingiris-launch

Or read: Gingiris Launch Playbook

💎 Premium Deep-Dive

Product Hunt Launch Action Guide — ¥99

  • Complete hour-by-hour timeline
  • Hunter selection framework
  • Community mobilization templates
  • Post-launch conversion playbook

Complete Launch Bundle — All 4 playbooks

🎯 1-on-1 Launch Coaching

Work directly with Iris (30x PH #1 Winner, AFFiNE Co-founder):

ServicePriceBest For
30 min session¥800Quick launch review
60 min session¥1500Full strategy session
Monthly retainer¥7000/moOngoing launch support

👉 Book now: gingiris.tools/services

Launch-Day Ops Addendum (2026 field data)

Hour-by-hour support hygiene

  1. Watch support quality and rate, not just the total. Historical launches were reviewed in roughly two-hour windows; abrupt bursts and low-context accounts were more likely to be removed. Treat ~100 upvotes/hour as an operational warning line, not an algorithm guarantee.
  2. If the count drops, pause amplification and audit sources. Do not compensate with another blast.
  3. Ask only real users and existing community members to visit voluntarily and leave an authentic comment. Never buy accounts or votes, coordinate vote exchanges, conceal identity/network origin, or instruct supporters to evade platform controls.

Decide which badge you're buying before you spend

Daily, weekly and monthly are not the same prize: weekly exposure ≈ 7x daily, monthly ≈ 30x. Aiming monthly → launch the second or third Tuesday of the month. Only need the daily badge → Friday–Sunday competition is thinner.

Budget and fit reality check

  • Do not infer a universal spend-to-vote rate. Historical outcomes varied by audience fit, account quality, assets, timing and platform review.
  • With a small budget, redirect effort: the field notes found that roughly ten relevant X KOC collaborations often beat a PH-only push on registrations. Historical directional benchmarks were PH exposure→upvote around 1% and Reddit OSS exposure→star around 5–8%; validate against your own cohort before budgeting.
  • One anonymized field case (an AI Agent product, 2025-12): a 500K-visit launch day engineered across Twitter/Reddit/YouTube/groups produced ~800 signups at $6.25 CAC. Traffic ≠ conversion — plan the funnel, not just the spike.
  • AFFiNE Apple Notes case (2024-02 launch): a three-person operations team coordinated 30 Twitter/LinkedIn KOLs and 24-hour multi-channel distribution. The project note reports 700K+ impressions and 1,000+ upvotes in 24 hours, monthly #8 within 96 hours, plus daily #2 and weekly #2. Treat these as self-reported historical figures—not a vote-per-KOL benchmark or causal guarantee.

Split launch day and leaderboard day

Route half your amplifiers to the website on announcement day, the other half to PH on badge day. Cramming both goals into 24 hours makes each dilute the other.

Evidence-backed operating sequence

  1. T−21 to T−14: make the maker profile credible through normal participation; finalize positioning and audience fit.
  2. T−14 to T−7: create Coming Soon, select a hunter only if their audience/feature access helps, and review every asset yourself. A hunter is not a source of votes.
  3. T−7 to T−1: observe recent competition for 1–3 weeks; prepare the maker comment, FAQ, demo, community destination and per-channel UTMs.
  4. Launch day: publish the first maker comment with the offer and community destination; answer questions; monitor source-level visits, activations and suspicious spikes every two hours.
  5. T+1 to T+7: move B2C leads into Discord/forum and B2B leads into Slack/community or a qualified demo flow. Report visits → signup → activation → retained/paid, not badge alone.

Historical planning heuristics: weekly visibility was roughly 7× daily and monthly roughly 30×; use the second/third Tuesday when attempting a monthly run, or Friday–Sunday when the goal is a less competitive daily launch. These are case observations, not current PH guarantees.

Source notes and field-case boundaries: references/iris-field-notes.md.

Related Skills

Author: Iris (生姜iris) | gingiris.tools

🔗 About the Author

Iris Wei — Growth consultant for 150+ AI startups. Ex-COO at AFFiNE (69K GitHub stars).

加入出海社群与购买资料包 · Community & Resources

  • 加入出海社群： 微信添加 staywildgrow，留言「加入社群」即可。

  • 购买资料包： 微信添加 staywildgrow，咨询购买 Gingiris 出海资料包，价格 599 元人民币。

  • Join the global growth community: Add staywildgrow on WeChat and send “加入社群” (join the community).

  • Get the resource pack: Add staywildgrow on WeChat to purchase the Gingiris global growth resource pack for CNY 599.

社群与资料包为可选入口，不影响本开源 Skill 的安装和使用。

The community and paid resource pack are optional; neither is required to install or use this open-source skill.

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 Product Hunt Playbook AI skill do?

It's 11pm before your launch. Your PH page is ready but you have no idea what actually moves the ranking algorithm. Upvotes? Comments? Time-of-day? This is the hour-by-hour operations manual from someone who's coached 30+ #1 finishes. What's inside: • Ranking algorithm deep-dive (it's not just upvotes — weighted factors decoded) • Launch day minute-by-minute checklist (PST timing, first 3 hours are critical) • Voter mobilization strategies (ethical, anti-spam-safe approaches) • Asset preparation templates (gallery, tagline ≤60 chars, maker comments) • Post-launch 72h momentum plan (convert...

Why use Product Hunt Playbook on TypingMind?

Because you install it once and use it with any model. Product Hunt Playbook 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 Product Hunt Playbook in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/product-hunt-playbook. 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 Product Hunt Playbook?

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 Product Hunt Playbook?

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

Is the Product Hunt Playbook AI skill free?

Yes. It is published on GitHub by Gingiris-1031 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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