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Ai Loading Ux

Listed
shipshitdev·vlatest·frontend

Helps with AI, UX design as part of agent workflows workflows.

Repository

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This skill no longer installs under the name ai-loading-ux. shipshitdev/skills has no skill named "ai-loading-ux" any more. It was renamed, moved or deleted upstream.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-27. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

shipshitdev

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: shipshit.dev.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    shipshit.devundeclared

    What it found

    • bundles/frontend/skills/ai-loading-ux/plugin.json:8contacts shipshit.dev
      "url": "https://shipshit.dev"
    • skills/ai-loading-ux/plugin.json:8contacts shipshit.dev
      "url": "https://shipshit.dev"

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned shipshitdev/library at a0f9899c42.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
a0f9899c42
Manifest
skills/ai-loading-ux/SKILL.md
Scanned
12 files, 2,306 lines
Copies in repo
2 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill, Ai Loading UX, provides design patterns and a decision framework to improve the user experience when users are waiting for AI-powered outputs. It focuses on how to visually represent different stages of AI processing—such as reasoning, multi-step task completion, content generation, or background processes—to build trust and reduce perceived wait times. The core principle is to provide appropriate feedback without overwhelming the user with unnecessary information.

When to use it

  • When designing an agent workflow that involves AI reasoning (e.g., complex question answering).
  • For tasks involving multiple sequential steps completed by the AI.
  • During content generation where output is streamed token-by-token.
  • When background processing occurs as part of a larger task.

Key capabilities

  • Decision Framework: Categorizes waiting scenarios (Reasoning Display, Progress Steps, Streaming States, Status Indicators) to guide design choices.
  • Pattern Categories: Offers specific design patterns for each category, with recommended approaches and anti-patterns.
  • Core Principles: Emphasizes the "Elevator Mirror Effect," Progressive Disclosure, balancing transparency, and signaling completion clearly.
  • Implementation Checklist: Provides a step-by-step guide for implementing AI loading states effectively.

Example prompts

  • "What's the best way to show users that the AI is reasoning through a complex question?"
  • "How should I visually represent progress when the AI is completing multiple steps in a task?"
  • "I need to display content being generated token-by-token. What patterns are recommended?"

Tips & gotchas

  • Consider the "Elevator Mirror Effect"—subtle animations can significantly reduce perceived wait times.
  • Avoid overwhelming users with too much detail; use progressive disclosure to reveal information only when requested.
  • Always signal completion clearly to avoid user frustration.