fire-number
GitHub计算FIRE目标金额及退休所需年限，提供敏感性分析表而非单一数值。通过Python脚本执行，强制标注所有假设（如收益率、提取率），并明确列出序列风险、税收等模型未涵盖因素，强调结果的范围性而非精确日期。
Trigger Scenarios
Install
npx skills add mohitagw15856/pm-claude-skills --skill fire-number -g -y
SKILL.md
Frontmatter
{
"name": "fire-number",
"description": "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
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
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.
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
Version History
- 54fad50 Current 2026-07-19 12:45
