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Shower

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LilMGenius
shower

Cold-read the artifact you're focused on from a fresh, zero-context sub-session to confirm it stands on its own — a shower-thought reset for accumulated session bias. Use when a long session has worn away your fresh eyes and you can no longer tell whether the artifact in focus is clear to someone with no prior context; before a handoff, publish, or merge; or when you want a clean-room comprehension smoke test. Spawns a separate context-free reviewer; it diagnoses, it does not fix.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill nameshower
Stars
1.3K
Forks
118
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 LilMGenius on GitHub. Read the source before you install it.

Installation

Install the Shower 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/LilMGenius/paperthin/tree/main/skills/depth/shower
  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/LilMGenius/paperthin.git /tmp/paperthin
mkdir -p .claude/skills
cp -r /tmp/paperthin/skills/depth/shower .claude/skills/shower
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Shower 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 Shower 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 Shower 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.

Step out of the session and let a clean mind read it: does the artifact stand on its own?

Goal

A long session quietly accumulates context you can't un-see, so you lose the ability to judge whether your work reads clearly to a first-timer — you no longer know what you left unsaid (the unknown-unknowns). The fix is a literally fresh brain: a separate sub-session that never saw this conversation cold-reads the artifact in focus and reports where a clean reader would stall. A smoke test for comprehension and handoff. This skill diagnoses; the fixing happens back in the main session.

Workflow

  1. Pin the scope: the artifact (or set) currently in focus. If "in focus" is ambiguous, confirm with the user or take the artifact just produced or under discussion. Privately note, in one line, what it is meant to be and who it is for — your yardstick for step 4; the reviewer never sees it.
  2. Launch a fresh sub-session (a subagent / Task — it starts context-free). Hand it the artifact's contents (inline, or a copy), not a repo path, and tell it not to open the project's README, docs, or neighboring files that would spoil the cold read. Give it nothing about your intent or reasoning.
  3. Have it cold-read blind and report, from the artifact alone:
    • what it takes the artifact to be, do, and expect;
    • what is unclear, ambiguous, or assumed-but-unstated;
    • what it would need to act confidently, and what it had to guess.
  4. Compare its blind understanding against the intent you noted in step 1 (which it never saw). Every mismatch is a defect in the artifact, not a reader error.
  5. Report the defects and concrete fixes, ordered by how badly each blocks a fresh reader.

Rules

  • The read MUST come from a separate, context-free sub-session — never self-assess in this session, because you can't un-see the context (that is the whole point).
  • Pass the artifact's contents, never your intent.
  • A forced "I had to assume…" is a finding, not a reader failure.
  • Medium-agnostic: adapt the cold-read questions to what the artifact is.
  • A single cold read is a single draw and can still be confidently wrong at high stakes; it is the default, not the ceiling of diligence, so escalate to multiple independent reads when the stakes justify the cost.
  • Read, don't sweep. The cold-read is an end-to-end read, not a pattern-grep — a sweep catches only known patterns and misses stale refs, dead links, fact drift, and silent edit-damage. Track how much was actually read; never report "clean" from a grep alone.

Verification

Before finishing:

  1. The read came from a fresh sub-session blind to your intent.
  2. Report the verdict (stands on its own / minor gaps / needs work) and hand the fixes to the main session.

Frequently asked questions

What does the Shower AI skill do?

Cold-read the artifact you're focused on from a fresh, zero-context sub-session to confirm it stands on its own — a shower-thought reset for accumulated session bias. Use when a long session has worn away your fresh eyes and you can no longer tell whether the artifact in focus is clear to someone with no prior context; before a handoff, publish, or merge; or when you want a clean-room comprehension smoke test. Spawns a separate context-free reviewer; it diagnoses, it does not fix.

Why use Shower on TypingMind?

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

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

Which AI models can use Shower?

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 Shower?

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

Is the Shower AI skill free?

Yes. It is published on GitHub by LilMGenius 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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