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Logical Thinking

Community
TheGoat395
logical-thinking

Determine what premises and evidence warrant.

Overview

PublisherTheGoat395
RepositoryCodex-Skills
Skill namelogical-thinking
Stars
136
Forks
48
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TheGoat395 on GitHub. Read the source before you install it.

Installation

Install the Logical Thinking 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/TheGoat395/Codex-Skills/tree/main/skills/logical-thinking
  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/TheGoat395/Codex-Skills.git /tmp/Codex-Skills
mkdir -p .claude/skills
cp -r /tmp/Codex-Skills/skills/logical-thinking .claude/skills/logical-thinking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logical Thinking 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 Logical Thinking 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 Logical Thinking 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.

Logical Thinking

Produce the strongest conclusion the available premises warrant, including unknown, conditional, or no conclusion when appropriate. Logical rigor means choosing the right inference system, preserving meaning and scope, checking each decisive step, and refusing to strengthen the result beyond its proof obligations.

Scale the proof to the question

Adapt the inference family; do not impose a universal template. Preserve meaning, units, scope and quantifiers. Test whether premises can hold while the conclusion fails; use decisive calculations, solvers, tests or observations. Correlated self-review is not independent verification.

For consequential, multi-step, ambiguous or contested inference, read proof and verification plus the applicable family below for maps, step checks, conclusion types and adversarial tests. Simple entailment/calculation remains direct.

Use conditional or unknown when warranted. Proven, must, causes, always and impossible require their own proof burden.

Normalize the problem

Before inference, identify:

  • exact question and candidate conclusion;
  • premises, observations, rules, and documentation;
  • source/status of each premise: given, observed, documented, inferred, assumed, or disputed;
  • definitions, entities, variables, units, timeframe, version, and domain;
  • quantifiers and modal force: all, some, none, must, may, usually, likely;
  • knowledge regime: closed world, open world, or incomplete real-world evidence;
  • hidden premises required to connect the stated information.

Resolve semantic ambiguity before formalizing. Do not create precision by translating an unclear sentence into symbols without deciding what the sentence means.

Choose the inference family

FamilyAppropriate questionValid conclusion form
DeductiveMust the conclusion follow?entailed, contradicted, or unknown
Defeasible/informalIs the argument presumptively reasonable?supported unless a critical exception succeeds
InductiveHow strongly do observations generalize?scoped probability or empirical support
AbductiveWhich explanation best accounts for the evidence?best current explanation, not proof
CausalWhat would happen under intervention or counterfactual change?association, intervention effect, or counterfactual claim
ProbabilisticHow should uncertainty update?posterior or calibrated range under assumptions
Documentary/ruleWhat does an authority establish here?governing, permitted, required, documented, or unresolved applicability
DecisionWhich action is preferable under uncertainty?recommendation conditional on goals and tradeoffs

For formal entailment, natural-language arguments, exceptions, or scheme-based reasoning, read formal and defeasible inference.

For causal, probabilistic, forecasting, or decision reasoning, read causal, probabilistic, and decision reasoning.

For conclusions from documentation, policies, tests, benchmarks, source code, or runtime evidence, read documentation and evidence conclusions.

Keep unknown open

Under an open-world or incomplete-evidence regime:

  • failure to prove C does not prove not C;
  • failure to prove not C does not prove C;
  • one absent fact does not license a closed-world assumption unless the system explicitly defines one;
  • inconsistent premises do not justify an arbitrary conclusion in ordinary analysis.

Classify a deductive target as:

  • Entailed: every model satisfying the premises satisfies C.
  • Contradicted: every model satisfying the premises satisfies not C, or C directly conflicts with a controlling premise.
  • Unknown: both C and not C remain compatible with the premises.
  • Premises inconsistent: the premise set itself requires repair before ordinary inference.

Check premise consistency first, then classify the target as entailed, contradicted or unknown; do not force a binary answer from incomplete evidence.

Coordinate boundaries

Use critical-thinking when the central question is whether evidence, assumptions, incentives, or argument quality deserves confidence. Use evidence-reconciliation when conflicting authority or current source state controls. Use deep-research when premises must be discovered. Use creativity when the missing work is generating possibilities rather than adjudicating what follows.

Deliver

For simple problems, apply the protocol silently and answer directly. For consequential problems, report the normalized premises, inference family, decisive rule or test, exceptions or unknowns, verification performed, and strongest warranted conclusion. Show enough structure to audit the result without dumping private scratch reasoning.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Logical Thinking AI skill do?

Determine what premises and evidence warrant.

Why use Logical Thinking on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TheGoat395/Codex-Skills/tree/main/skills/logical-thinking. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Logical Thinking?

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 Logical Thinking?

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

Is the Logical Thinking AI skill free?

Yes. It is published on GitHub by TheGoat395 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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