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Growth Loops

CommunityPopular
phuryn
growth-loops

Identify growth loops (flywheels) for sustainable traction. Evaluates 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms, building product-led traction, or understanding how growth loops work.

Overview

Publisherphuryn
Repositorypm-skills
Skill namegrowth-loops
Stars
26.4K
Forks
2.8K
Bundled files
Instructions only
LicenseMIT
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 phuryn 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.
  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/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/pm-go-to-market/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

Overview

Identify and design growth loops (flywheels) that create sustainable traction. This skill evaluates five proven growth loop mechanisms to reduce reliance on paid acquisition and build product-led growth.

When to Use

  • Designing growth mechanisms for a product
  • Building sustainable viral or referral traction
  • Reducing reliance on paid acquisition
  • Analyzing competitor growth strategies
  • Optimizing product for product-led growth

The 5 Growth Loop Types

1. Viral Loop

Product content created by users gets shared on external platforms, bringing new users back to the product.

  • Mechanism: Users create content in-product → Share on social/external platforms → New users discover and signup
  • Example: Figma designs shared as links, Loom videos shared in emails
  • Strength: Exponential user acquisition if content is inherently shareable
  • Challenge: Requires highly shareable output and strong incentive to share

2. Usage Loop

Users create content or value within the product, then share it, which invites new users or drives re-engagement.

  • Mechanism: User creates → Shares creation → Others consume → Become engaged users
  • Example: Twitter threads, Medium articles, Notion templates shared publicly
  • Strength: Growth tied directly to product usage and network effects
  • Challenge: Requires content creation friction to be very low

3. Collaboration Loop

Users invite colleagues to co-create or collaborate within the product, expanding the user base within organizations.

  • Mechanism: User creates → Invites colleagues for collaboration → Colleagues discover product value
  • Example: Google Docs invitations, Figma team projects, Slack channels
  • Strength: Deep organizational penetration and high retention
  • Challenge: Works best for collaborative/team-based products

4. User-Generated Loop

Users discover new content or features through other users' creations, then create and share their own content.

  • Mechanism: User discovers content → Creates similar content → Shares creation → Others discover
  • Example: TikTok, Pinterest, YouTube trends driving creator participation
  • Strength: Creates content flywheel and network effects
  • Challenge: Requires critical mass of quality content to sustain

5. Referral Loop

Users invite other potential users in exchange for rewards, incentives, or social recognition.

  • Mechanism: User refers → Referred user joins → Referrer gets reward → Shares more referrals
  • Example: Dropbox referral bonus, Uber rider referrals, PayPal signup bonuses
  • Strength: Directly incentivizes acquisition; easy to measure ROI
  • Challenge: Requires valuable incentive without eroding unit economics

How It Works

Step 1: Define Product Value

Clarify the core value users experience:

  • Primary action users take in your product
  • Value created per user action
  • Network effects present (if any)
  • Friction points in the experience

Step 2: Evaluate Loop Fit

Assess which growth loops align with your product:

  • Product type (collaborative, content-based, utility, etc.)
  • Target user behavior and sharing habits
  • Network effects already present
  • Existing user base and engagement

Step 3: Design Loop Mechanics

Create specific loop implementation:

  • Trigger that initiates sharing or invitations
  • Incentive for participation (intrinsic or extrinsic)
  • Ease of sharing mechanism
  • Conversion rate from invite to activation
  • Frequency of loop repetition per user

Step 4: Calculate Loop Coefficient

Estimate growth velocity:

  • Invites/shares per user per cycle
  • Conversion rate of invites to new users
  • Net new users per cycle
  • Time per cycle iteration

Step 5: Build the Loop

Implement the highest-leverage loop first:

  • Start with the most natural loop for your product
  • Optimize messaging and friction
  • Measure loop metrics and conversion rates
  • Compound results over time

Input Format

Use $ARGUMENTS to pass:

  • Product description and primary user action
  • Target user demographics and behavior
  • Existing sharing/collaboration features
  • Current growth channels and metrics
  • Constraints or opportunities

Output

A growth loops analysis including:

  • Ranked evaluation of all 5 loop types for your product
  • Recommended primary growth loop with implementation plan
  • Secondary loops to layer over time
  • Key metrics and measurement framework
  • 30-60-90 day implementation roadmap
  • Potential loop coefficient and growth projections

Framework

Based on growth loops research by Ognjen Bošković. Focuses on compounding user acquisition through built-in, product-native sharing and collaboration mechanisms.

Tips

  • Start with one loop and master it before adding complexity
  • Viral loops compound fastest but take time to build
  • Collaboration loops create strongest retention and LTV
  • Measure loop health weekly during optimization phase
  • Combine loops for multiplicative effect once operating at scale

Further Reading

Frequently asked questions

What does the Growth Loops AI skill do?

Identify growth loops (flywheels) for sustainable traction. Evaluates 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms, building product-led traction, or understanding how growth loops work.

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/phuryn/pm-skills/tree/main/pm-go-to-market/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?

Yes. It is published on GitHub by phuryn 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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