fire-number

GitHub

计算财务独立(FIRE)目标及所需年限,提供敏感性分析网格并明确标注所有假设。适用于咨询退休时间、FIRE金额或储蓄轨迹建模的场景,拒绝虚假精确度,强调风险披露与诚实模型。

exports/openclaw/fire-number/SKILL.md mohitagw15856/pm-claude-skills

Trigger Scenarios

询问我的FIRE数字是多少 询问何时可以提前退休 需要多少资金才能实现财务独立 希望模拟我的储蓄轨迹

Install

npx skills add mohitagw15856/pm-claude-skills --skill fire-number -g -y
More Options

Non-standard path

npx skills add https://github.com/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/fire-number -g -y

Use without installing

npx skills use mohitagw15856/pm-claude-skills@fire-number

指定 Agent (Claude Code)

npx skills add mohitagw15856/pm-claude-skills --skill fire-number -a claude-code -g -y

安装 repo 全部 skill

npx skills add mohitagw15856/pm-claude-skills --all -g -y

预览 repo 内 skill

npx skills add mohitagw15856/pm-claude-skills --list

SKILL.md

Frontmatter
{
    "name": "fire-number",
    "homepage": "https:\/\/mohitagw15856.github.io\/pm-claude-skills\/skill\/fire-number.html",
    "metadata": {
        "openclaw": {
            "emoji": "🧮"
        }
    },
    "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 12:20

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Metadata

Files
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Version
471c606
Hash
77c99646
Indexed
2026-07-19 12:20

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