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Factchk

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LilMGenius
factchk

Verify reality-grounded claims against external sources in both directions before they ship — could the 'absurd' be real, could the 'obvious' be false or long-established? Use whenever an artifact, or the sentence you are about to write, asserts something as plausible, realistic, absurd, novel, or impossible from intuition rather than a checked source; before relying on a factual claim in prose, a design rationale, a research claim, or a plan. Fires on metacognitive doubt — when you can't actually know, verify instead of trusting the feeling. Scans read-only, then fixes the clear errors or flags the judgment calls; leaves deliberate fiction alone.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill namefactchk
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 Factchk 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/factchk
  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/factchk .claude/skills/factchk
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Factchk 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 Factchk 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 Factchk 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.

Check what's asserted as real against reality — in both directions — before it ships.

Goal

The author's "plausible / absurd / novel" is the least reliable line in any artifact. Human priors fail both ways — they exclude the real (desert frogs exist) and normalize the impossible (weightless crates). factchk scans claims presented as reality-grounded, verifies each against external sources in both directions, and fixes or flags. Its scope is a claim about the world — whether what's asserted as real is real — not the soundness of a validation or the survival of a whole plan.

Workflow

  1. Scan for reality-grounded assertions — anything leaning on "this is plausible / realistic / absurd / novel / impossible because X," including the claim you are about to write, not only those already on the page.
  2. Verify each against external sources (web search, open references) in both directions: could the "absurd" be real? could the "obvious" or "novel" be false or long-established? If you cannot reach a source, flag — never assert from intuition.
  3. Fix or flag: a mechanically-clear error (a wrong date, a misattributed source, a falsified number) → correct it with a cited source; a judgment call (a contested or interpretive claim) → surface it, don't silently rewrite.
  4. Report each claim, its verdict, the source, and — for a failed claim — which direction it failed.

Rules

  • Target claims asserted as reality-grounded, never deliberate fiction ("in our world, boxes float" is a declared choice). When it is unclear whether a claim is an in-world choice or a real-world assertion, flag, don't fix — the hardest call in the skill.
  • Flagging only the unrealistic catches half the errors.
  • The trigger is metacognition, not knowledge: you can't know every domain, so the moment you are about to assert a checkable fact from a feeling, verify it instead of trusting the feeling.
  • A pass that finds nothing changes nothing.

Verification

Before finishing: every verdict traces to a citable external source, not a reworded prior intuition — and the report reads as fixes-vs-flags, not restated intuitions.

Frequently asked questions

What does the Factchk AI skill do?

Verify reality-grounded claims against external sources in both directions before they ship — could the 'absurd' be real, could the 'obvious' be false or long-established? Use whenever an artifact, or the sentence you are about to write, asserts something as plausible, realistic, absurd, novel, or impossible from intuition rather than a checked source; before relying on a factual claim in prose, a design rationale, a research claim, or a plan. Fires on metacognitive doubt — when you can't actually know, verify instead of trusting the feeling. Scans read-only, then fixes the clear errors or...

Why use Factchk on TypingMind?

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

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

Which AI models can use Factchk?

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 Factchk?

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

Is the Factchk 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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