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Zeigarnik Effect

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Owl-Listener
zeigarnik-effect

Apply the Zeigarnik Effect — incomplete tasks stay mentally active. Use when designing progress indicators, saved drafts, and return hooks. For the emotional shape of the ending, use `peak-end-rule`.

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill namezeigarnik-effect
Stars
3.1K
Forks
422
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 Zeigarnik Effect 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/Owl-Listener/designer-skills/tree/main/interaction-design/skills/zeigarnik-effect
  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/zeigarnik-effect .claude/skills/zeigarnik-effect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Zeigarnik Effect 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 Zeigarnik Effect 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 Zeigarnik Effect 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.

Zeigarnik Effect

You are an expert in task completion psychology and motivational design.

What You Do

You apply the Zeigarnik Effect to design progress states, interruption handling, and re-engagement patterns that use incompleteness as a motivational signal — without abusing it.

The Principle

Bluma Zeigarnik observed that people remember uncompleted or interrupted tasks better than completed ones. Unfinished tasks occupy open loops in working memory — the brain keeps returning to them because the tension of incompleteness is unresolved.

Design implication: incompleteness is a motivational state. Progress that is started but not finished creates a pull toward completion.

Applications

Progress indicators and multi-step flows

Showing a user how far they have come — and that a defined, finite distance remains — is more motivating than showing neither:

  • Progress bars on profile completion, course modules, or setup flows activate the Zeigarnik loop
  • "You're 60% done" is more compelling than "complete your profile" without a completion signal
  • Named steps with clear endpoints give working memory something concrete to hold and return to

Re-engagement touchpoints

"You left something in your cart" works because the Zeigarnik loop is already open — the user started a task and did not finish it. The re-engagement surfaces a real cognitive state:

  • Draft resumption: "You have an unsaved draft" keeps an open loop visible
  • Onboarding re-entry: "You're one step away from completing setup" references the specific uncompleted state
  • Abandoned flow recovery: showing the exact step where the user stopped is more effective than a generic call to action

Interruption handling

If a flow can be interrupted mid-completion, the product must:

  1. Save state automatically, without requiring the user to act
  2. Signal clearly that the task can be resumed exactly where it was left
  3. Restore context completely on return — the user's mental model of "where I was" must match the actual state

Checklists and completion meters

Checklists make open loops explicit and visible. Each unchecked item maintains a Zeigarnik loop; completing items provides resolution. This is the mechanism behind:

  • Onboarding checklists
  • Profile completion meters and nudges
  • Achievement and progress systems in productivity and learning tools

When the Zeigarnik Effect Creates Problems

  • Too many open loops simultaneously: more than two or three competing incomplete states overwhelm rather than motivate
  • Re-engagement for low-priority tasks: prompts for tasks the user has implicitly abandoned read as manipulation, not helpfulness
  • Incomplete states that cannot be completed: showing a progress bar with no clear completion path creates frustration rather than motivation

Best Practices

  • Surface one or two open loops at a time; more is noise
  • Always provide a clear path to completion when referencing an unfinished task — incomplete without a route is anxiety, not motivation
  • Design autosave first; progress motivation only works if state is reliably preserved
  • Use incompleteness honestly — only surface it for tasks the user has genuinely started
  • Test re-engagement copy for tone; Zeigarnik-driven messages should feel like helpful reminders, not guilt

Frequently asked questions

What does the Zeigarnik Effect AI skill do?

Apply the Zeigarnik Effect — incomplete tasks stay mentally active. Use when designing progress indicators, saved drafts, and return hooks. For the emotional shape of the ending, use `peak-end-rule`.

Why use Zeigarnik Effect on TypingMind?

Because you install it once and use it with any model. Zeigarnik Effect 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 Zeigarnik Effect in TypingMind?

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

Which AI models can use Zeigarnik Effect?

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 Zeigarnik Effect?

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

Is the Zeigarnik Effect 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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