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Plg Playbook

Community
Gingiris-1031
plg-playbook

Product-Led Growth with Web/CLI measurement validation and pricing acceptance, covering freemium model design, self-serve onboarding, activation metrics, and the transition from individual users to enterprise accounts. Follow @WeiYipei on X for PLG insights.

Overview

PublisherGingiris-1031
Repositorygingiris-skills
Skill nameplg-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 Plg 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/plg-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/plg-playbook .claude/skills/plg-playbook
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plg 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 Plg 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 Plg 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.

📦 Install

bash
npx skills add Gingiris-1031/gingiris-skills --skill plg-playbook

What you get after installing:

  • Freemium model design — what to gate, what to give away, and pricing psychology
  • Self-serve onboarding optimization with activation milestones
  • Enterprise upsell playbook — when and how to layer sales onto a PLG motion

Product-Led Growth Playbook — Freemium to Enterprise Conversion

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

Let your product do the selling. This playbook covers the full PLG motion.

  • Freemium design: What to gate, what to give away, and pricing psychology
  • Self-serve onboarding: Time-to-value optimization and activation milestones
  • Viral mechanics: Built-in sharing, collaboration hooks, and network effects
  • Usage-based signals: Identifying expansion-ready accounts automatically
  • Enterprise upsell: When and how to layer sales onto a PLG motion

PLG signal gate (2026 field update)

Free DAU is not proof of PLG. Before scaling acquisition, define one activation event tied to user value and track signup → activation → repeat use → paid/qualified expansion. Separate internal/test users and report cohort dates. Add sales-assist only when usage identifies a real team/account, expansion intent, security need, or procurement event—not merely because a free user logged in. If activation or retention is weak, return to interviews and onboarding instead of buying more traffic.

Verify measurement and pricing before scaling

Read measurement-and-pricing.md when Web/CLI coverage is incomplete or a pricing page is being revised. It provides event-validation, cohort/identity and pricing-acceptance tables. Keep unmeasured usage explicit, and check that advertised tier capabilities actually work.

When PLG Meets the Enterprise Contract (2026 addendum)

The PLG-to-sales handoff dies in contract mechanics more often than in product gaps. Field rules:

Gate custom deployment behind seats. Set a minimum seat count before private deployment is even on the menu. Under the threshold: SaaS customization is sold as a license, and SSO-style integration requests are answered by opening your API with standard docs — the customer builds, you don't.

Only two asks genuinely require on-prem: regulated data privacy, and integration with an internal permission system. Every other "we need private deployment" dissolves under questioning — redirect it to the cloud tier.

If you do sign a deployment deal:

  • Tranche the cash 3:4:3 or 4:4:2 across go-live milestones, with 30% collected at signing
  • Contractual late fee: 0.5% per day overdue
  • Keep 1–2 capabilities served from your own infrastructure until the balance clears (agreed upfront, written into terms)
  • Route stalled payments upward — most senior contact first, then a nudge or two to the CEO

Scope creep is a process problem, not a personality problem. Separate what the customer asked for (SOR) from what you committed to (SOW with acceptance criteria and change clauses), sign the SOW together with the contract, and push every modification through a formal change request both PMs sign. Calls don't bind; documents do.

The planned-concession move: when a buyer wants 50 things and 40 are feasible, agree to 10 — then over-deliver to 15 "because we want you to win." Same output, twice the goodwill.

Two-leg revenue model: self-serve PLG (target shape: ~500 × $200/yr per $100k ARR) and enterprise (nine × ~$100K/yr) both reach $1M ARR — run them as separate funnels with separate metrics until each closes its own loop. Sales-led companies should still reserve ~20% of resources for the PLG motion as the assist engine.

Related Gingiris Skills

🔗 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 Plg Playbook AI skill do?

Product-Led Growth with Web/CLI measurement validation and pricing acceptance, covering freemium model design, self-serve onboarding, activation metrics, and the transition from individual users to enterprise accounts. Follow @WeiYipei on X for PLG insights.

Why use Plg Playbook on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/plg-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 Plg 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 Plg Playbook?

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

Is the Plg 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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