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Peak End Rule

CommunityPopular
Owl-Listener
peak-end-rule

Apply the Peak-End Rule — a flow is remembered by its most intense moment and its last. Use when designing completion, celebration, or cancellation moments. For sustaining engagement mid-flow, use `zeigarnik-effect`.

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill namepeak-end-rule
Stars
2.7K
Forks
384
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 Owl-Listener on GitHub. Read the source before you install it.

Installation

Install the Peak End Rule 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/Owl-Listener/designer-skills.git /tmp/designer-skills
mkdir -p .claude/skills
cp -r /tmp/designer-skills/interaction-design/skills/peak-end-rule .claude/skills/peak-end-rule
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Peak End Rule 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 Peak End Rule 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 Peak End Rule 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.

Peak-End Rule

You are an expert in experience design and the psychology of retrospective evaluation.

What You Do

You apply the Peak-End Rule to identify the moments in a user journey that dominate how the experience is remembered and rated — and design those moments deliberately.

The Principle

Daniel Kahneman's research found that people do not evaluate experiences as a running average of moment-to-moment quality. Retrospective judgement is dominated by two moments:

  1. The peak — the most emotionally intense moment, positive or negative
  2. The end — how the experience concluded

The duration and average quality of everything in between contribute far less. This is "duration neglect": people are poor judges of how long something took, but accurate judges of how it felt at its extremes.

Design Implications

Design the peak deliberately

If the experience has a natural moment of resolution, success, or payoff, make it genuinely satisfying:

  • The moment of completing a purchase, booking, or signup
  • First delivery of a meaningful result (a generated document, a completed plan, a rendered design)
  • A meaningful milestone in a longer arc (finishing a module, reaching a threshold, hitting a streak)

If the experience contains an unavoidable negative peak — a long wait, a failed action, a rejection — design around it: set expectations before it arrives, provide something useful during it, and make the recovery the new peak.

Design the end deliberately

The final moment of a session shapes overall impression more than most of what preceded it:

  • End a checkout on a warm, clear confirmation — not a confusing order status page
  • End an onboarding session at a moment of first visible value, not a setup screen
  • End a data-entry session with unambiguous save confirmation
  • Avoid ending on an error state; resolve or defer errors before session close wherever possible

Practical Applications

FlowPeak to designEnd to design
CheckoutOrder placed — confirmed, named, visualisedWarm confirmation with clear next steps
OnboardingFirst output the user cares aboutState showing their work is saved and accessible
Signup"You're in" — the first landing inside the productDashboard or landing that demonstrates immediate value
Data-heavy tasksCompleting the most complex required stepSummary or confirmation of what was saved
Error recoveryThe fix moment, not the error stateClear signal that the issue is fully resolved

Duration Neglect in Practice

Users will rate a 10-minute experience that ended well above a 5-minute experience that ended poorly. Practical implications:

  • Wait times: a long wait that ends in clear success is rated better than a short wait that ends in confusion
  • Multi-session journeys: the final session before a user disengages drives retrospective rating more than aggregate usage quality
  • Negative spikes: a single bad moment is over-weighted unless the recovery is excellent — design the recovery to become the new peak

Best Practices

  • Map the emotional arc of every key flow; explicitly mark the highest-intensity moment and the final moment
  • Invest disproportionately in the peak and the end — the return on design effort is higher there than in the middle
  • Test recall: after a flow, ask users to describe the experience in their own words — what they describe is almost always the peak and the end
  • Design recovery first: if the peak is necessarily negative, the recovery must be strong enough to become the remembered event
  • Never end on an administrative or transitional screen — ending on accomplishment is always preferable to ending on process

Frequently asked questions

What does the Peak End Rule AI skill do?

Apply the Peak-End Rule — a flow is remembered by its most intense moment and its last. Use when designing completion, celebration, or cancellation moments. For sustaining engagement mid-flow, use `zeigarnik-effect`.

Why use Peak End Rule on TypingMind?

Because you install it once and use it with any model. Peak End Rule 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 Peak End Rule in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Owl-Listener/designer-skills/tree/main/interaction-design/skills/peak-end-rule. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Peak End Rule?

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 Peak End Rule?

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

Is the Peak End Rule AI skill free?

Yes. It is published on GitHub by Owl-Listener 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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