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Happy Path Scope

Community
Productfculty-aipm
happy-path-scope

Use this skill when the user asks to "scope this to the happy path", "what's the minimum I need to prototype", "help me cut scope for prototyping", "what should I include in the prototype", "how do I decide what to prototype", or needs help deciding which parts of a feature to include in a prototype vs. which to defer. Also use at the start of any prototyping session to right-size the prototype scope.

Overview

PublisherProductfculty-aipm
RepositoryPM-Copilot-by-Product-Faculty
Skill namehappy-path-scope
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 Happy Path Scope 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/happy-path-scope
  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/happy-path-scope .claude/skills/happy-path-scope
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Happy Path Scope 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 Happy Path Scope 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 Happy Path Scope 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.

Happy Path Scoping

You are helping the user scope a prototype to the minimum needed for its purpose. Prototypes fail most often because they're over-scoped — too many screens, too many states, too much detail — which makes them slow to build, hard to iterate, and confusing to test.

Framework: Colin Matthews (prototype scope discipline), Shape Up (appetite-based scoping applied to prototypes).

Key principle: A prototype should contain only what's needed to answer the question it's designed to answer. Every screen and state beyond that is waste.

Step 1 — Clarify the Prototype's Purpose

Before scoping, ask: what is this prototype for?

User testing: The prototype needs to be realistic enough that users can complete the task without confusion. Prioritize: realistic content, working flow, correct information architecture. De-prioritize: visual polish, error states, edge cases.

Stakeholder demo / alignment: The prototype needs to communicate the vision. Prioritize: key screens, the "wow moment", one complete flow. De-prioritize: completeness, all edge cases, mobile version.

Engineering spec / handoff: The prototype needs to demonstrate the interaction mechanics. Prioritize: interactive states, transitions, behavior on action. De-prioritize: visual perfection, backend-dependent behavior.

Team alignment / discussion: The prototype needs to ground a conversation. Prioritize: the two or three screens in dispute. De-prioritize: everything else.

Step 2 — Define the Happy Path

The happy path is the sequence of steps where:

  • The user knows exactly what to do
  • Every input is valid
  • Every system response succeeds
  • The user achieves their goal

Map the happy path for the feature being prototyped:

  1. [Step 1] — [User action]
  2. [Step 2] — [System response]
  3. [Step 3] — [User action] ... N. [Outcome] — [User has achieved their goal]

This becomes the spine of the prototype. Everything else is a branch — don't prototype branches.

Step 3 — Cut List

For each of the following, decide: In prototype or Out of prototype

ElementInclude?Why
Auth / login flowOUTStart logged in. Auth is a solved problem.
Empty statesOUTUse mock data instead.
Error messages / validationOUTHappy path only.
Loading statesOUTMock instant load.
Settings / configurationOUTNot in the happy path.
Admin / internal viewsOUTNot the user's journey.
Mobile responsiveOUTUnless this IS a mobile test.
[Feature-specific element][In/Out][Reason]

Step 4 — Minimum Screen Count

Force a minimum screen count:

For user testing: You need at least 3 screens (start state, mid-flow, success state). You rarely need more than 7.

For stakeholder demo: You need the "before" (the problem state) and the "after" (the solution working). 2–4 screens is typical.

For engineering spec: You need every unique interaction state. Count the number of distinct interactions, not screens.

If the prototype has more than 8 screens, scope it down until it fits. Prototype one flow completely rather than two flows partially.

Step 5 — Mock Data Requirements

Define the mock data needed to make the prototype feel real:

  • How many items in each list? (3–5 is almost always enough)
  • What are the realistic values? (Use real names, real product language, real numbers)
  • What's the "interesting" example? (The one that best illustrates the feature's value)

Step 6 — Output

Produce:

  • Happy path definition (numbered steps)
  • Screen list (only what's in scope)
  • Cut list (what's excluded and why)
  • Mock data requirements
  • Prototype scope statement: "This prototype shows [user type] doing [action] to achieve [goal]. It does NOT show [list of exclusions]."

This scope statement should be shared whenever the prototype is shared, to prevent misunderstanding about what's implemented.

Frequently asked questions

What does the Happy Path Scope AI skill do?

Use this skill when the user asks to "scope this to the happy path", "what's the minimum I need to prototype", "help me cut scope for prototyping", "what should I include in the prototype", "how do I decide what to prototype", or needs help deciding which parts of a feature to include in a prototype vs. which to defer. Also use at the start of any prototyping session to right-size the prototype scope.

Why use Happy Path Scope on TypingMind?

Because you install it once and use it with any model. Happy Path Scope 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 Happy Path Scope 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/happy-path-scope. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Happy Path Scope?

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 Happy Path Scope?

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

Is the Happy Path Scope 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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