Prism logo

Prism

CommunityPopular
LilMGenius
prism

Split one artifact — a claim, plan, or file — across 2 to 5 independent lenses, one per genuinely distinct failure mode (correctness, security, readability, cost, adversarial-user), and return their convergence: where they agree, where they disagree, and the single next question that resolves the disagreement. Use when one reviewer isn't enough because the failure modes are heterogeneous, or a claim looks strong to its author and needs cross-lens pressure before it ships.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill nameprism
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 Prism 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/mesh/prism
  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/mesh/prism .claude/skills/prism
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prism 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 Prism 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 Prism 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.

Split one artifact across independent lenses and return where they converge and where they don't.

Goal

The result is a picture of where the lenses agree, where they disagree, and the smallest question that would resolve the disagreement. Not a checklist, and never an average — averaging the lenses is the failure this skill exists to prevent.

Workflow

  1. Read the artifact end to end before choosing the lenses.
  2. Choose one lens per genuinely distinct failure mode — correctness, security, readability, cost, adversarial-user, and the like. Two candidate lenses that share a failure mode are one lens, not two. In practice this lands between 2 and 5: below 2 there is nothing to converge, and past 5 you are almost always padding with lenses that overlap. There is no default count — the artifact's distinct failure modes set it.
  3. Through each lens, produce a one-line verdict — pass, fail, or unclear — with its single most load-bearing reason. No hedging list; if a reason isn't load-bearing, drop it.
  4. Compare the verdicts and group them: full agreement, agreement for different reasons, disagreement.
  5. Where there is disagreement, name the single next question whose answer would move it into agreement or a clean split with owners. That question is the output, not the individual verdicts.
  6. Where every lens agrees, return the shared verdict with the reasons that were load-bearing for at least two lenses.

Rules

  • Independent lenses, not roles. A "security reviewer" and a "senior security reviewer" are one lens, not two.
  • One reason per lens — the load-bearing one. A lens that returns a list is hedging.
  • Disagreement is the product. A prism that agrees on everything did not need to be run.
  • Never average. If two lenses split, the answer is the question that resolves the split, not the midpoint of their verdicts.
  • Draw the lenses fresh for the artifact in hand. A lens useful for a spec may be useless for a migration; don't carry lenses across artifacts.
  • Don't manufacture disagreement. A run that finds none returns the shared verdict and stops.
  • User-invoked on purpose: plural lenses are an opt-in spend, and their convergence must never masquerade as automatic proof, so this fires only when deliberately reached for.

Verification

The result reads as a picture, not a checklist — a reader sees which lenses agreed, which disagreed, and the one question that matters, and can act on it without re-running the prism. If they'd need to re-run it to know what to do next, step 5 was skipped.

Frequently asked questions

What does the Prism AI skill do?

Split one artifact — a claim, plan, or file — across 2 to 5 independent lenses, one per genuinely distinct failure mode (correctness, security, readability, cost, adversarial-user), and return their convergence: where they agree, where they disagree, and the single next question that resolves the disagreement. Use when one reviewer isn't enough because the failure modes are heterogeneous, or a claim looks strong to its author and needs cross-lens pressure before it ships.

Why use Prism on TypingMind?

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

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

Which AI models can use Prism?

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 Prism?

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

Is the Prism 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇