Estimate

Skill chrisdavidson/first-principles-skill/first-principles/skills/estimate

Claude Code plugin for systematic first-principles thinking — a 5-phase methodology (identify essence, challenge assumptions, establish ground truths, reason upward, validate) plus 13 companion skills. Installable via the Claude Code plugin marketplace; every conclusion traces back to a verified ground truth, not to analogy or convention.

Install

npx -y skills add chrisdavidson/first-principles-skill --skill estimate

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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What its author says it does

Copied from the file, not written here

Runs a focused estimate only — magnitude rebuild from units. Invoke via /estimate only.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.6 KB, as published. Nobody here has run it

Focused Estimate Mode

You are running in focused-estimate mode. Execute only the procedure below and produce only its canonical output sections — do not run the full 5-phase first-principles analysis. Skip Step 0 technique selection; the user has already chosen this technique by invoking the slash command directly.

When to reach for this

Use estimate when a decision hinges on the order of magnitude of a quantity and you do not have — or cannot trust — a direct lookup. The question you are really asking is: "What is this number, rebuilt from what I actually know?"

Good fit: the target quantity has units that can be reconstructed as a product of factors whose individual values are pinnable to physical constants, definitions, or direct measurements; a rough bracket (lower/central/upper) is sufficient to drive the decision; and an analogy-based estimate ("it's probably like X project") would be unverifiable.

Not a good fit: the problem involves weighing options against criteria with different importance — that is trade-off analysis, which scores options on weighted criteria rather than rebuilding a magnitude. It is also not the right tool when the task is to verify whether a compound claim's constituents reduce to irreducible primitives — that is the five-whys reduce-to-primitives mode, which traces a claim's structure down to physical laws and definitions rather than rebuilding a quantity's order of magnitude.

Decision rule — choosing among the three quantitative-ish techniques:

Estimate = quantitative magnitude rebuild: HOW BIG is this quantity? Reconstructs a target number from constituent unit-factors (dimensional analysis). Stops when the factor product yields a bracketed magnitude — lower, central, upper.
Trade-off = qualitative weighted scoring: WHICH option wins on weighted criteria? Assigns importance weights to criteria before scoring, preventing the reverse-engineering of weights to justify an intuitive pick.
Five-whys (reduce-to-primitives) = definitional/physical reduction: WHAT is this claim made of? Traces constituent sub-claims to irreducible primitives (physical laws, definitions, direct measurements). Stops when every branch bottoms out at an anchor.

A single analysis often uses all three: decompose the performance claim to find what factors matter, estimate the magnitude of each factor, and apply trade-off if several technically viable options remain.

Procedure

Name the target quantity and its units (e.g., "$/kWh of delivered storage") before decomposing.

Decompose into unit-factors and show the cancellation. List the sub-quantities that multiply to the target's units, confirming they cancel correctly — dimensional analysis. Show the unit arithmetic explicitly (e.g., "kg/kWh × $/kg × 1/cycles → $/kWh").

Assign a first-principles value to each factor, sourced from one of:

A physical constant or definition (e.g., specific heat capacity) — traceable and invariant.
A direct measurement (e.g., a datasheet spec) — empirically anchored.

Do not cite a similar past project as the value — "a comparable project cost X" is an analogy, not a first-principles value. If no first-principles value exists, flag it as assumed with a defensible range.

Compute the central magnitude by multiplying the factors' central values, with the unit arithmetic explicit.

Bracket the result. For each uncertain factor, substitute its conservative and aggressive values to compute the lower and upper ends: [lower bound, central estimate, upper bound]. A Fermi estimate without an explicit bound range is incomplete — the bracket, not the single central value, is the deliverable.

Apply the decision-resolution stop criterion. The estimate is "good enough" when both the bracket's lower and upper ends drive the same decision. If the bracket spans an order of magnitude and straddles the decision threshold, tighten the dominant uncertain factor with a better measurement or escalate the uncertainty explicitly.

Read estimate-detail.md when you need:

a worked example of this technique
the failure modes and how to avoid them
handoff guidance to another technique

If a fuller analysis is needed afterward, invoke the main first-principles agent with this output as Known ground truths.