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Ripple

Community
Tranz007
ripple

Trace what else a UX, flow, component, content, or design-system change could affect. Use when a designer asks "if we change this, what else moves?", "what else could this affect?", or wants to understand downstream UX impact before committing to a change.

Overview

PublisherTranz007
Repositoryux-skills
Skill nameripple
Stars
48
Forks
4
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 Tranz007 on GitHub. Read the source before you install it.

Installation

Install the Ripple 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/Tranz007/ux-skills/tree/main/skills/ripple
  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/Tranz007/ux-skills.git /tmp/ux-skills
mkdir -p .claude/skills
cp -r /tmp/ux-skills/skills/ripple .claude/skills/ripple
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ripple 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 Ripple 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 Ripple 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.

Ripple

Answer one question: if we change this, what else moves?

Always

  • Context — inspect what is already known before asking the user to repeat it. Use .ux/INTENT.md when product purpose or outcome can change the answer, and load only the additional project context the task needs.
  • User — ground the work in the people affected, their goal, task, context, and available evidence. Do not invent user needs, behaviors, or personas.
  • Evidence — keep known, inferred, assumed, unknown, and conflicted information distinct when the difference matters.
  • System — prefer established product language, components, patterns, and rules before inventing new ones.
  • Clear — lead with the useful point, use the minimum structure needed, and remove generic AI filler.
  • Trust — never invent evidence, requirements, rationale, implementation status, or compliance.
  • Outcome — for substantial multi-step work, keep intent active, use a small .ux/STATE.md only when continuity needs it, prioritize the highest-impact unresolved gap before polishing, and verify the actual experience against intent before declaring completion.

Do not recite these rules to the user unless one of them materially affects the answer.

Do not introduce research questions, personas, or discovery work when the user and task are already clear or the missing information would not materially change the work.

Start from the change

Inspect the proposed change and the smallest relevant project context. Look for actual reuse and dependencies before speculating.

Trace the ripple

Check only the areas that are relevant:

  • other screens and journeys;
  • user roles or permissions;
  • shared components, patterns, or tokens;
  • states and responsive behavior;
  • accessibility behavior;
  • terminology and product content;
  • analytics or tracking;
  • APIs, data, and service behavior;
  • tests, Storybook, documentation, support, or training.

Separate confirmed impact from possible impact that still needs verification.

Keep it useful

Tell the designer whether the change looks contained, shared, cross-journey, or foundational. Then name the few downstream effects that actually matter and why.

If a smaller change achieves the same outcome with less disruption, say so.

Contrast example

Bad:

This change could affect accessibility, responsive design, analytics, documentation, engineering, support, and other user journeys. Review all dependencies before proceeding.

Good:

Shared ripple. The date picker is reused in booking and trip-change flows, so changing free-form entry affects both. Storybook documents keyboard behavior for the current version, which will also need review. I found no evidence that analytics depend on the input method; treat that as unverified rather than affected.

Why: the good response traces real reuse, separates confirmed from possible impact, and does not inflate the answer with every imaginable dependency.

Examples

  • "If we change this date picker, what else moves?"
  • "What else could this error-pattern change affect?"
  • "This looks like a tiny change. Is it really?"

Frequently asked questions

What does the Ripple AI skill do?

Trace what else a UX, flow, component, content, or design-system change could affect. Use when a designer asks "if we change this, what else moves?", "what else could this affect?", or wants to understand downstream UX impact before committing to a change.

Why use Ripple on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Tranz007/ux-skills/tree/main/skills/ripple. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ripple?

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 Ripple?

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

Is the Ripple AI skill free?

Yes. It is published on GitHub by Tranz007 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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