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first-principles-thinking

first-principles-thinking

github.com/ysskrishnaProductivity1880 installsUpdated 2w ago
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About first-principles-thinking

first-principles-thinking is a structured skill for re-examining assumptions behind a strategy, design, cost, or process. It guides analysis from evidence and defensible fundamentals to a rebuilt conclusion and its implications.

Key features

  1. 01State the reconstruction target and analysis pass
  2. 02Surface conventions and hidden comparisons
  3. 03Question assumptions and their origins
  4. 04Separate fundamentals from assumptions
  5. 05Derive conclusions and implications

Use cases

  1. 01Reassess a design copied from an incumbent
  2. 02Review costs or processes kept by habit
  3. 03Evaluate whether a strategy's assumptions hold

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ysskrishna/ai-agent-skills --skill first-principles-thinking

After installing, type/first-principles-thinkingin Claude Code to confirm it loaded.

Bundled filesreferences/example.md

Details

From the project README

First Principles Thinking

Question inherited baggage. Rebuild only from bedrock you can defend.

When to use

  • "Why do we do it this way?", "is this really required?", "what would this cost if we started from scratch?"
  • A strategy or design copied from an incumbent ("like Uber for X") that nobody has checked.
  • Cost, architecture or process choices that exist only because they always have.

Skip: quick convention-following checklists with no wish to revisit assumptions, or purely social coordination with nothing to model.

Before you start

  1. State in one block: Reconstruction target (belief, cost, design or strategy to ground) and Pass (Surface, Question, Bedrock, Rebuild, Implications).
  2. Gather first. Read the code, contracts, specs or data that show what is actually required (a regulation, an SLA, a measured limit) before tagging anything fundamental. Ask up to 3 questions only for immutable constraints that tools cannot answer.
  3. Light path. Small ask: 3 assumptions, 3 bedrock items, a 3-step rebuild, 2 implications.

Steps

1. Surface

State the conventional answer or the analogy people rely on. List loaded words and hidden comparisons.

2. Question

For each major assumption: Assumption: ... - Why believed? (authority, analogy, experience) - What if false?

3. Bedrock

List truths that survive scrutiny: physics, logic, arithmetic, legal musts, documented needs of real users. Tag each [FUNDAMENTAL] or [ASSUMPTION].

Aim for three or more honest bedrock items. If fewer exist, say why in one line.

4. Rebuild

Derive conclusions in numbered steps using only [FUNDAMENTAL] items. "Industry standard" is not a premise unless translated into a fundamental (for example "buyers require SLA X because regulation Y"). A new premise goes into Bedrock first, with a tag.

5. Implications
  • So what: what changes versus the conventional path.
  • Cost of being wrong if a tagged assumption fails.
  • A short vs convention contrast when it helps the decision.

If bedrock is too thin to rebuild, write insufficient grounding and list the evidence that would fix it.

Pitfalls

  • Calling a preference or a habit "fundamental".
  • Rebuilding from first principles and arriving exactly at the old answer. That is fine if each step is defended; say so.
  • Faux profundity. Keep every step short and checkable.
  • Ignoring switching cost. A better design from scratch may not beat the current one after migration.

Worked example: references/example.md.

Checklist

  • Target and Pass stated
  • Surface names the convention or analogy
  • Question ties each assumption to why it is held
  • Bedrock tagged [FUNDAMENTAL] or [ASSUMPTION]
  • Rebuild uses only fundamentals
  • Implications name what changes and the cost of being wrong

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