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Thinking First Principles

CommunityPopular
tjboudreaux
thinking-first-principles

When a constraint is treated as fixed, separate physics from convention, keep only independently supported primitives, and rebuild the simplest solution that satisfies real constraints.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-first-principles
Stars
1.3K
Forks
158
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 tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking First Principles 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.
  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/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-first-principles .claude/skills/thinking-first-principles
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking First Principles 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 Thinking First Principles 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 Thinking First Principles 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.

First Principles

Strip assumed constraints to independently supported primitives, then rebuild. Escape analogy when convention fails or looks artificially expensive.

When to Use

  • A constraint is treated as fixed ("too expensive," "impossible," "always done this way") without independent support.
  • Convention or analogy has failed, stalled, or only yields weak incremental variants.
  • Greenfield design where industry defaults may smuggle false requirements.
  • Cost or feasibility claims rest on pricing, precedent, or authority rather than physics, math, or measured need.

When NOT to Use

  • The constraint is verified physics, hard regulation, or measured capacity → accept it and optimize within it.
  • A proven standard library, protocol, or known-good pattern already fits → use it; do not re-derive.
  • Time-critical incidents → act on the most likely cause first; reserve first-principles rebuild for post-incident redesign.
  • Incremental polish of a working system with no false-constraint signal → skip.

Procedure

  1. State the problem and claimed constraints. List every limit treated as fixed (cost, scale, stack, process, "must use X").
  2. Classify each constraint. Tag as physics/math, regulation/contract, measured fact, or convention/analogy/authority. Keep only the first three as binding unless evidence upgrades a convention.
  3. Decompose to primitives. Required inputs, conservation/complexity bounds, true non-negotiables. Demand independent support (measurement, derivation, primary requirement)—not vendor pricing or competitor precedent.
  4. Discard unsupported assumptions. For each convention tag, state a falsifier. Drop or renegotiate anything lacking support.
  5. Rebuild from remaining primitives only. Simplest solution that satisfies binding constraints and ignores artificial ones. Prefer commodity inputs and fewer moving parts over analogy.
  6. Validate and stop. Check against real physics, contracts, and measured needs. Define the cheapest kill test. Stop when a coherent rebuild plus discriminating validation exists, or when only verified hard constraints remain.

Output

Emit a first-principles rebuild:

  • claimed_constraints: list with classification tags
  • primitives: independently supported truths only
  • discarded_assumptions: conventions dropped and why
  • rebuild: solution derived only from primitives
  • binding_residuals: real limits that remain
  • kill_test: cheapest check that would falsify the rebuild

Verification

  • Independence check: every primitive must cite measurement, derivation, or primary requirement—not precedent alone.
  • No smuggled analogy: if the rebuild still depends on "how others do it" without a primitive, strip it.
  • Constraint honesty: verified physics/regulation must remain; do not wish them away.
  • Over-application guard: if a standard solution is already optimal under verified constraints, do not rebuild for novelty.
  • Stop: one decompose → classify → rebuild → kill-test pass; do not recurse without new evidence.

Frequently asked questions

What does the Thinking First Principles AI skill do?

When a constraint is treated as fixed, separate physics from convention, keep only independently supported primitives, and rebuild the simplest solution that satisfies real constraints.

Why use Thinking First Principles on TypingMind?

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

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

Which AI models can use Thinking First Principles?

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 Thinking First Principles?

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

Is the Thinking First Principles AI skill free?

Yes. It is published on GitHub by tjboudreaux 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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