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Growth Loops

Community
Productfculty-aipm
growth-loops

Use this skill when the user asks about "growth loops", "viral loops", "product-led growth", "PLG", "how does our product grow itself", "referral mechanics", "word of mouth", "how to build growth into the product", "organic growth", or wants to identify and design the mechanisms through which the product acquires more users through its own usage.

Overview

PublisherProductfculty-aipm
RepositoryPM-Copilot-by-Product-Faculty
Skill namegrowth-loops
Stars
80
Forks
34
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Productfculty-aipm on GitHub. Read the source before you install it.

Installation

Install the Growth Loops 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/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/growth-loops
  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/Productfculty-aipm/PM-Copilot-by-Product-Faculty.git /tmp/PM-Copilot-by-Product-Faculty
mkdir -p .claude/skills
cp -r /tmp/PM-Copilot-by-Product-Faculty/skills/growth-loops .claude/skills/growth-loops
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Growth Loops 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 Growth Loops 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 Growth Loops 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.

Growth Loops

You are helping the user identify and design growth loops — self-reinforcing cycles where product usage generates new users, which generates more usage, compounding over time.

Framework: Lenny Rachitsky (growth loops), Andrew Chen (network effects and viral growth), Brian Balfour (loops vs. funnels).

Key insight: Funnels leak. Loops compound. A product with a strong growth loop doesn't need to keep increasing acquisition spend — each cohort of users generates the next cohort.

Step 1 — Load Context

Read memory/user-profile.md for product stage, business model, and current bets. Read context/product/personas.md for user type and behavior patterns.

Step 2 — Growth Loop Types

Identify which types of loops are possible for this product:

Viral / social loops: User uses product → creates something shareable → shares with others → new users discover the product → they sign up Example: User creates a shareable PRD → shares on LinkedIn → peers see it and want to create their own

Content loops: Users generate or curate content → content is indexed → search brings new users → they become users and generate more content Example: PM shares their research synthesis publicly → Google indexes it → other PMs find it and discover PM Copilot

Word of mouth loops (engineered): User gets value → built-in mechanism prompts sharing → they share → referral drives new user Example: After a successful PRD, PM Copilot surfaces: "Your PRD looks great. Want to share it with your team or post it to LinkedIn?"

Integration loops: User integrates product into their stack → other users in the same stack discover it → they request access Example: User connects PM Copilot to Linear → Linear ticket shows "Generated with PM Copilot" → other team members ask to use it

Product-led loops: Users invite others to get more value → invited users also get value → they invite more users Example: Stakeholder receives PM Copilot-generated update → quality of update prompts them to ask for the tool → they become users

Network effect loops: Product gets better as more users join → better product attracts more users Example: Shared memory layer across teams → team templates improve → more PMs adopt it

Step 3 — Loop Identification

For the user's specific product, identify:

  • Which loop types are structurally possible given the product's nature?
  • Which loops already exist (even informally)?
  • Which loops would be highest-leverage to engineer?

Rate each possible loop on:

  • Potential magnitude: How many new users could each iteration of this loop generate?
  • Friction to activate: How hard is it for a user to complete the loop action?
  • Speed: How quickly does the loop cycle? (Days vs. weeks vs. months)
  • Build effort: How much engineering is needed to create or improve this loop?

Step 4 — Loop Design

For the top 1–2 loops identified:

Map the full loop: Start → Action → Output → New User Touchpoint → Conversion → Back to Start

Identify the friction points: Where does the loop break or slow down?

Design the intervention: What product or marketing change would close the loop or reduce friction?

Measurement: How will you know if the loop is working? (What's the loop conversion rate? How many new users does one loop cycle generate?)

Step 5 — PLG vs. Sales-Assisted

Based on the ICP and product:

  • Can the product sell itself (PLG) or does it need sales assist?
  • If PLG: what's the free-to-paid trigger? What in-product experience drives conversion?
  • If sales-assisted: where does the loop end and the sales conversation begin?

For PM Copilot: PLG is the natural motion — users install the plugin themselves. The loop is: user uses PM Copilot → generates a high-quality output → shares output → peer asks "how did you make that?" → discovers and installs PM Copilot.

Step 6 — Output

Produce:

  • Loop identification (which loops exist and which are possible)
  • Loop prioritization (highest leverage × lowest effort)
  • Full loop design for top 1–2 loops
  • Measurement framework for each loop (inputs, outputs, cycle rate)
  • First experiment to test whether a proposed loop actually works

Frequently asked questions

What does the Growth Loops AI skill do?

Use this skill when the user asks about "growth loops", "viral loops", "product-led growth", "PLG", "how does our product grow itself", "referral mechanics", "word of mouth", "how to build growth into the product", "organic growth", or wants to identify and design the mechanisms through which the product acquires more users through its own usage.

Why use Growth Loops on TypingMind?

Because you install it once and use it with any model. Growth Loops 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 Growth Loops in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/growth-loops. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Growth Loops?

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 Growth Loops?

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

Is the Growth Loops AI skill free?

It is published on GitHub by Productfculty-aipm. 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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