Fire Number

Compute a financial-independence (FIRE) target and years-to-reach with every assumption labeled as an assumption — plus a sensitivity table instead of a single false-precision answer. Use when asked what's my FIRE number, when can I retire early, how much do I need to be financially independent, or model my savings trajectory. Produces the FIRE number, years-to-target at stated assumptions, a return × withdrawal-rate sensitivity grid, and the honest list of what the model ignores.

FIRE Number Skill

Every FIRE calculation is three assumptions wearing a number's clothing: a withdrawal rate, a real return, and the pretense that returns arrive in a convenient order. This skill does the math properly and refuses the false precision — the deliverable is a surface of outcomes with the assumptions labeled, not a single date to organize a life around.

What This Skill Produces

• The FIRE number — annual spend ÷ withdrawal rate, with the withdrawal rate named as the choice it is
• Years to target — at stated savings, contributions, and real return
• The sensitivity grid — years-to-target across return (3/5/7%) × withdrawal rate (3/3.5/4%)
• The ignored-risks list — sequence-of-returns, taxes, spending drift — stated, not buried

Required Inputs

Ask for these if not provided:

• Current invested savings (invested — not home equity, not emergency cash)
• Monthly contribution (realistic, not aspirational — ask which)
• Target annual spend in retirement (today's dollars; if unknown, current spend is the honest starting guess, labeled)
• Return and withdrawal assumptions (defaults: 5% real, 4% withdrawal — both labeled as defaults)

Programmatic Helper

bash

python3 scripts/fire_number.py --savings 120000 --monthly 3000 --spend 60000
python3 scripts/fire_number.py --savings 120000 --monthly 3000 --spend 60000 --return 5 --wr 4 --json

Deterministic monthly compounding at a constant real return (inflation already removed — never stack an inflation adjustment on top). The script prints the sensitivity grid and its own not-modeled list.

Framework: The Honesty Rules

• The 4% rule is a study, not a law — one country, one era, 30-year horizons; early retirees have longer horizons, which is why the grid includes 3% and 3.5%
• Sequence risk is unmodeled and largest near the finish — a crash in year 1 of retirement ≠ a crash in year 20; say this every time
• Real vs nominal discipline — everything here is in today's dollars; mixing in nominal market returns (~+3%) silently is the classic error
• A range is the deliverable — "17–24 years depending on returns" is honest; "August 2043" is astrology with a spreadsheet

Output Format

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FIRE Analysis: [name/scenario]

The Number and the Path

[Script output: FIRE number, years at base assumptions, sensitivity grid]

Reading the Grid

[Two sentences: the realistic range, and which assumption the user's plan is most hostage to.]

What This Model Ignores

Sequence-of-returns risk (largest near the target date) · taxes · spending drift · [anything scenario-specific].

The One Lever

[Of spend, contribution, and return: which change moves the date most for THIS user — usually spend, since it hits both sides.]

Educational model, not financial advice — verify with a licensed professional before acting on it.

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Quality Checks

• Every assumption is labeled at point of use, not in a footnote
• The sensitivity grid appears — never a single years-to-target alone
• Real-vs-nominal is explicit and consistent
• Sequence-of-returns risk is named
• The disclaimer line appears in the artifact

Anti-Patterns

• Do not output a retirement date — output a range with its drivers
• Do not stack inflation adjustments on a real return, or skip them on a nominal one
• Do not treat 4% as physics — it's a parameter the grid varies
• Do not count home equity or emergency funds in invested savings without flagging it
• Do not present the model's output without its assumptions