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Atomize Claim

Organization
yogsoth-ai
atomize-claim

Decompose a compound position into atomic, independently challengeable/falsifiable claims and expose implicit assumptions.

Overview

Publisheryogsoth-ai
Repositoryde-anthropocentric-research-engine
Skill nameatomize-claim
Stars
505
Forks
43
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepository
  • 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 yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Atomize Claim 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/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/atomize-claim
  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/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/atomize-claim .claude/skills/atomize-claim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Atomize Claim 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 Atomize Claim 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 Atomize Claim 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.

atomize-claim

Purpose

Split a compound claim into atomic propositions that can receive separate evidence and confidence judgments.

Input contract

yaml
required: [compound_claim]
optional: [argument_context, domain_terms, evidence_records]
constraints: [atomic claims retain logical connectors and scope qualifiers]

Procedure

  1. Identify subjects, predicates, qualifiers, conditions, and logical connectors.
  2. Split independent propositions while preserving dependency and quantifier scope.
  3. Mark implicit premises and ambiguous terms.
  4. Emit an ordered atomic claim set with recomposition rules.

Output contract

yaml
produces: [atomic_claims, logical_connectors, implicit_premises, recomposition_map]
delta_fields: [findings, uncertainties, open_questions]

Quality gates

  • Recombining the atoms preserves the original meaning.
  • Each atom is independently evidence-addressable.

Failure and counterexamples

Do not split a definition or condition from the proposition it qualifies.

Provenance map

  • resolved: atomize-claim

Frequently asked questions

What does the Atomize Claim AI skill do?

Decompose a compound position into atomic, independently challengeable/falsifiable claims and expose implicit assumptions.

Why use Atomize Claim on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/atomize-claim. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Atomize Claim?

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 Atomize Claim?

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

Is the Atomize Claim AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 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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