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Resilient Localization

Community
RealTask
resilient-localization

Harden interfaces against slow networks, unexpected data, long text, localization, right-to-left layouts, and recoverable failures.

Overview

PublisherRealTask
RepositoryMAX-DESIGN
Skill nameresilient-localization
Stars
4
Forks
0
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 RealTask on GitHub. Read the source before you install it.

Installation

Install the Resilient Localization 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/RealTask/MAX-DESIGN/tree/main/skills/resilient-localization
  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/RealTask/MAX-DESIGN.git /tmp/MAX-DESIGN
mkdir -p .claude/skills
cp -r /tmp/MAX-DESIGN/skills/resilient-localization .claude/skills/resilient-localization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Resilient Localization 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 Resilient Localization 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 Resilient Localization 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.

Resilience and localization

When to use

Use when preparing a UI for real-world data, multiple languages or locales, slow or unreliable networks, permissions, asynchronous content, varied input lengths, or production release hardening.

Workflow

1. Inspect the supported environment

Determine supported locales, date and number formats, text direction, browsers, input modes, network expectations, permissions, and data contracts. Reuse existing localization and error-handling architecture instead of introducing another layer.

2. Keep content localizable

Use stable message keys and complete messages rather than concatenating sentence fragments. Support placeholders, plural rules, contextual translator notes, and locale-aware formatting for dates, times, numbers, and currency. Never assume translated strings have the same length as English.

3. Support direction and expansion

Check right-to-left layouts when in scope. Prefer logical CSS properties such as inline-start and block-end when appropriate. Test long labels, compound words, different scripts, larger text settings, and missing translations. Avoid embedding essential text in images.

4. Model failure and delay

For asynchronous operations, define idle, loading, success, empty, stale, offline, partial, error, and retry states when relevant. Preserve safe user input across failure. Provide cancellation or retry when feasible. Do not show fake progress, fabricate data, or imply completion before the operation succeeds.

5. Harden against unpredictable data

Check null and missing fields, unusually long strings, unexpected units, large and negative values, empty arrays, duplicate items, stale records, expired sessions, permission denial, and partial responses. Truncate visually only when the full value remains discoverable where important; never silently destroy user input.

6. Make layouts resilient

Use intrinsic sizing and flexible constraints instead of fixed heights that clip translated text. Ensure tables, sidebars, dialogs, toasts, and navigation can accommodate expansion. Avoid layout shifts as fonts and media load. Keep essential actions available at narrow widths and with zoom.

7. Protect user trust

Explain what can be recovered and what was saved. Use precise timestamps or freshness labels when relevant. Confirm only real outcomes. Log technical details appropriately without exposing secrets or unnecessary personal data in user-facing messages.

8. Validate conditions

Test with at least one long-string scenario, an alternate locale if supported, empty and malformed data, slow or failed requests, and responsive layouts. Test RTL only when the product supports it. Clearly mark environments and cases that were not tested.

Quality gates

  • Copy and formats use the project's localization conventions.
  • Long translations and RTL behavior are considered when in scope.
  • Network, permission, empty, stale, and failure states reflect actual system status.
  • Unexpected and missing data does not break or misrepresent the interface.
  • User input is preserved across recoverable errors where safe.
  • Layouts remain usable at narrow widths and increased text sizes.
  • Sensitive technical details are not exposed to end users.
  • Verification claims match the environments and scenarios actually tested.

Frequently asked questions

What does the Resilient Localization AI skill do?

Harden interfaces against slow networks, unexpected data, long text, localization, right-to-left layouts, and recoverable failures.

Why use Resilient Localization on TypingMind?

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

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

Which AI models can use Resilient Localization?

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 Resilient Localization?

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

Is the Resilient Localization AI skill free?

Yes. It is published on GitHub by RealTask 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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