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Design For Repetition

Community
kemiljk
design-for-repetition

Improve workflows used repeatedly by examining recurring effort, waiting, setup, interruption, context retention, and expert paths. Use when an experience works once but may become tiring or inefficient in regular use.

Overview

Publisherkemiljk
Repositoryskills
Skill namedesign-for-repetition
Stars
64
Forks
3
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 kemiljk on GitHub. Read the source before you install it.

Installation

Install the Design For Repetition 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/kemiljk/skills/tree/main/skills/design-for-repetition
  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/kemiljk/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/design-for-repetition .claude/skills/design-for-repetition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design For Repetition 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 Design For Repetition 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 Design For Repetition 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.

Design for Repetition

Evaluate a workflow across repeated use, while preserving what someone needs to learn it and perform it safely. Familiarity changes the experience; it does not make everyone an expert.

Establish the pattern of use

Infer the audience, task, frequency, consequence, and conditions of interruption from the request and available evidence. Identify assumptions when usage data or research is absent. Repetition can mean several operations in one session or returning after a long gap; those require different support.

Follow the named task once before proposing shortcuts. Then inspect what repeats: setup, navigation, decisions, data entry, waiting, feedback, and recovery. Preserve the user's scope. Reviews produce recommendations; requests to improve or implement authorise bounded changes.

Find the accumulating cost

Recurring burdenUseful directionPreserve
Re-entering stable contextSuitable defaults or retained settingsVisible current context and a way to change it
Repeated navigationA discoverable direct path or return positionOrientation and a usable ordinary path
Repeated item operationsBatch action when items share valid semanticsItem scope, partial-failure reporting, and recovery
Waiting for nonessential effectsEarlier access to the completed resultTruthful completion feedback and relevant explanation
Repeated consequential decisionsRemember safe choices or offer a suitable presetReview where context, cost, or reversibility warrants it
Interrupted workRetain the useful draft or place in the taskAn account of what was saved and what remains pending

Measure task effort, not clicks alone. An extra comparison step may reduce costly mistakes. A shorter path may demand more memory or hide the current scope. Treat timing and error observations as evidence from the sampled conditions, not universal usability results.

Support familiarity without requiring it

Keep the primary route understandable. Add keyboard shortcuts, repeat actions, presets, or batch operations only when they solve a supported recurring need. Make faster paths discoverable and avoid moving essential actions exclusively into shortcuts or hover states.

Retain context at the appropriate boundary. Decide whether a setting belongs to a task, session, project, device, or account. A remembered destination or filter can become stale; expose it before an action when a mistaken context would matter. Do not persist sensitive or destructive choices merely to remove a step.

Distinguish harmless acknowledgement from consequential confirmation. Simplify repetitive feedback when it obstructs use, while retaining confirmation, undo, or review where the action warrants it. Never remove an established approval requirement to make a workflow faster.

Evaluate motion and celebration over repeated encounters. Useful feedback should remain legible and interruptible where appropriate. Do not assume every expressive detail becomes annoying; inspect whether it delays work, competes with attention, or still carries meaning.

Try the repeated and interrupted paths

Use representative sequences rather than a prescribed number of loops. Check first use, a repeat with context retained, a changed context, and an interrupted return when these apply. For batch changes, inspect mixed eligibility and partial success if the real workflow supports them.

For example, a person exporting several project revisions may benefit from retained format settings and immediate access to each finished file. Verify that changing projects does not silently retain the wrong destination. This is a hypothetical scenario, not a reproduced bug.

In a working implementation, exercise changed paths with relevant input methods and realistic data. A repeated automated sequence can expose mechanical friction; it does not establish that people feel less fatigue. Seek user evidence when the decision depends on comfort or learning. Disclose checks that remain unavailable.

For any reported bug, include the affected consumer and task, prerequisites and steps, expected and actual behaviour, and impact. Separate an observed failure from a hypothesis about cumulative frustration.

Deliver

Explain the recurring burden, the bounded change, what the ordinary path retains, and the evidence or experiment supporting the recommendation. Implement scoped improvements when requested. Stop when the identified repetition cost is addressed without hiding necessary context, weakening recovery, or expanding the workflow into a redesign.

Frequently asked questions

What does the Design For Repetition AI skill do?

Improve workflows used repeatedly by examining recurring effort, waiting, setup, interruption, context retention, and expert paths. Use when an experience works once but may become tiring or inefficient in regular use.

Why use Design For Repetition on TypingMind?

Because you install it once and use it with any model. Design For Repetition 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 Design For Repetition in TypingMind?

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

Which AI models can use Design For Repetition?

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 Design For Repetition?

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

Is the Design For Repetition AI skill free?

Yes. It is published on GitHub by kemiljk 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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