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Bet Sizing

Community
assimovt
bet-sizing

Evaluate product bets and shape pitches using Shape Up's appetite model and Bezos's Type 1/Type 2 decision framework. Use when asked to assess a product bet, evaluate initiative risk, decide resource allocation, or shape a pitch for a new feature or project.

Overview

Publisherassimovt
Repositoryproductskills
Skill namebet-sizing
Stars
71
Forks
6
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 assimovt on GitHub. Read the source before you install it.

Installation

Install the Bet Sizing 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/assimovt/productskills/tree/main/skills/bet-sizing
  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/assimovt/productskills.git /tmp/productskills
mkdir -p .claude/skills
cp -r /tmp/productskills/skills/bet-sizing .claude/skills/bet-sizing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bet Sizing 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 Bet Sizing 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 Bet Sizing 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.

Size product bets by separating reversible from irreversible decisions and shaping work to fit an appetite. Most product bets are Type 2 decisions — reversible, low-cost to try, high-cost to deliberate. Move fast on those. Save deliberation for Type 1 decisions that are hard to undo.

Type 1 vs Type 2 Decisions (Bezos)

Type 1 (Irreversible): One-way doors. Hard to undo once committed.

  • Choosing a core technology/platform
  • Pricing model changes that affect existing customers
  • Killing a product line
  • Public commitments (partnerships, integrations)

Type 2 (Reversible): Two-way doors. Easy to undo or iterate.

  • Most new features (can ship, measure, remove)
  • UI/UX changes (can A/B test or revert)
  • Internal tooling decisions
  • Most API additions (harder to remove, but additive is safer)

Rule: Use lightweight process for Type 2. Use deliberate process for Type 1. Most product teams over-process Type 2 decisions and under-process Type 1 decisions.

Shape Up Pitch Format

When proposing a bet, structure it as a Shape Up pitch:

1. Problem

A specific story showing real pain. Not an abstract need — a concrete situation with a real user.

"When a PM finishes a customer interview, they spend 45 minutes transcribing notes into a PRD. By the time they're done, the emotional context is gone and the PRD reads like a requirements list."

2. Appetite

How much time is this worth? Not how long it will take — how much you're willing to invest.

  • Small bet: 1-2 weeks
  • Medium bet: 3-4 weeks
  • Large bet: 6 weeks (maximum for Shape Up)

If you can't fit the solution in the appetite, reshape or kill it.

3. Solution

Breadboard-level, not pixel-perfect. Show the key interactions and flows without getting into visual design. Fat-marker sketches, flow diagrams, or written walkthroughs.

4. Rabbit Holes

Known risks and unknowns that could blow up the timeline. For each: what's the risk and how will you mitigate it?

5. No-Gos

What's explicitly excluded. This is as important as what's included — it prevents scope creep during execution.

Expected Value Assessment

For larger bets, estimate expected value:

EV = (Upside x P(success)) - (Downside x P(failure)) + Learning Value

  • Upside: Best-case outcome (metric improvement, revenue, users)
  • P(success): Probability it works (be honest — most features have 30-50% success rate)
  • Downside: Cost if it fails (time, opportunity cost, technical debt)
  • Learning value: What you'll learn even if it fails. High learning value makes negative-EV bets worthwhile for early-stage products.

Guidelines

  • CRITICAL: Classify every decision as Type 1 or Type 2 before deciding how much process to apply.
  • NEVER spend 6 weeks deliberating a Type 2 decision. Ship it, measure it, adjust.
  • NEVER rush a Type 1 decision because of artificial urgency. These are worth slowing down for.
  • ALWAYS include No-Gos in a pitch. Without explicit exclusions, scope will grow.
  • ALWAYS include Rabbit Holes. The risks you name are less dangerous than the ones you don't.
  • NEVER pitch without a stated appetite. "Build this" without a time budget is an open invitation to over-engineer.

Built on Shape Up (Basecamp) and Jeff Bezos's Type 1/Type 2 decision framework. Skills from productskills.

Frequently asked questions

What does the Bet Sizing AI skill do?

Evaluate product bets and shape pitches using Shape Up's appetite model and Bezos's Type 1/Type 2 decision framework. Use when asked to assess a product bet, evaluate initiative risk, decide resource allocation, or shape a pitch for a new feature or project.

Why use Bet Sizing on TypingMind?

Because you install it once and use it with any model. Bet Sizing 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 Bet Sizing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/assimovt/productskills/tree/main/skills/bet-sizing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bet Sizing?

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 Bet Sizing?

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

Is the Bet Sizing AI skill free?

Yes. It is published on GitHub by assimovt 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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