analyze-japan-debt-service-tax-burden

analyze-japan-debt-service-tax-burden is a skill for Claude Code, Codex from fatfingererr/macro-skills. It costs 74 tokens per session (4,544 once invoked), scanned A, original, MIT.

A public-data analysis workflow for estimating how much of Japan's tax revenue is used for government debt interest and testing how higher bond yields could increase that burden.

In plain words
What is it for?
Use it to calculate interest-to-tax ratios, estimate the average financing cost, model refinancing scenarios, compare debt size with other economies, and assign risk levels.
Why use it?
It separates interest payments from broader debt-service costs, so claims about Japan's debt pressure use clearly defined measures and assumptions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is python scripts/generate_charts.py --full --output-dir ../../output.

Good fit Use it to calculate interest-to-tax ratios, estimate the average financing cost, model refinancing scenarios, compare debt size with other economies, and assign risk levels.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/fatfingererr/macro-skills
agentmods
npx agentmods add skills/fatfingererr/macro-skills/analyze-japan-debt-service-tax-burden

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin analyze-japan-debt-service-tax-burden/plugin install analyze-japan-debt-service-tax-burden after adding the marketplace above.

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README.md
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Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,544 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00074 $0.04544
Opus 5 $0.00037 $0.02272
Sonnet 5 $0.00015 $0.00909
Haiku 4.5 $0.00007 $0.00454

Measured 12d ago against content hash 682f1567929a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyze-japan-debt-service-tax-burden scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/data_manager.py, scripts/fetch_jgb_yields.py, scripts/fetch_tic_holdings.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analyze-japan-debt-service-tax-burden/SKILL.md · 390 lines

How it starts

The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.

分析日本債務利息負擔 Skill

以公開數據量化日本「利息吃掉稅收」的敘事,提供可驗證的現況核對、敏感度分析與風險分級。

<essential_principles>

interest_tax_ratio = interest_payments / tax_revenue

這是影片敘事「利息吃掉 1/3 稅收」的可核驗版本。不同口徑(國稅 vs 一般會計稅收 vs 總收入)會產生不同數值,必須明示口徑選擇。

口徑對照(FY2025)

口徑 計算 比例
純利息/稅收 10.5兆/70兆 15.0%
國債費/稅收 28.2兆/70兆 40.3%

注意:媒體敘事「利息吃掉 1/3」通常誤用國債費(含本金)口徑。

implied_avg_rate = interest_payments / debt_stock

衡量存量債務的平均融資成本。對比當前市場利率可評估再融資壓力。

FY2025:10.5兆 / 1,324兆 = 0.79% vs 當前 10Y 殖利率 2.0%+ → 差距反映大量存量債務在低利率時期發行,未來再融資將推高利息負擔。

媒體常用「美國等效規模」表達日本債務以增強震撼效果。

公式(動態計算):

debt_to_gdp = japan_debt_stock / japan_gdp
debt_in_us_terms = us_gdp × debt_to_gdp

數據來源:GDP 從 FRED 實時抓取,非硬編碼。

範例:$30.6T × 250% = $76.5T ≈ $70T(媒體口語化)

用途:解釋影片/新聞中「$70T」數字的來源,用於跨國比較時統一規模感知。

把殖利率變動映射到利息支出增加:

additional_interest ≈ debt_stock × pass_through × delta_yield

其中 pass_through 是年度再定價/再融資比例(約 15%),delta_yield 以小數表示(200bp = 0.02)。

區間 interest_tax_ratio 含義
🟢 綠 < 0.25 財政彈性充足
🟡 黃 0.25–0.40 財政彈性開始下降
🟠 橘 0.40–0.55 政策空間明顯受限
🔴 紅 > 0.55 接近「2/3」敘事區域

所有輸出必須標示:

  • 稅收口徑(national_tax / general_account_tax / total_revenue)
  • 利息口徑(interest_only / debt_service)
  • 資料年度與滯後(lag)
  • 再定價假設(pass_through)

</essential_principles>

<quick_start>

最快的方式:執行快速檢查

cd skills/analyze-japan-debt-service-tax-burden
pip install pandas numpy requests matplotlib  # 首次使用
python scripts/japan_debt_analyzer.py --quick

輸出範例:

{
  "yield_stats": {"tenor": "10Y", "latest": 1.23, "percentile": 0.97},
  "fiscal": {"interest_tax_ratio": 0.15, "risk_band": "green"},
  "headline": "利息支出佔稅收 15.0%,處於🟢 GREEN 區",
  "data_sources": {"jgb_10y": "FRED/IRLTLT01JPM156N", "fiscal": "config/FY2025"}
}

完整分析(含實時數據刷新)

python scripts/japan_debt_analyzer.py --full --refresh

生成視覺化 Dashboard

python scripts/generate_charts.py --full --output-dir ../../output

輸出:output/japan_debt_dashboard_YYYYMMDD.png

Dashboard 包含:

  • Interest/Tax Ratio 風險儀表盤
  • 殖利率分位數指標
  • 財政數據摘要
  • 壓力測試情境比較圖

生成債務螺旋模擬圖表

# 完整多情境螺旋模擬
python scripts/generate_spiral_chart.py --all --output-dir ../../output

# 單一壓力情境(如 +200bp)
python scripts/generate_spiral_chart.py --stress 200 --output-dir ../../output

# 自定義模擬年數
python scripts/generate_spiral_chart.py --years 15 --output-dir ../../output

輸出:output/japan_debt_spiral_YYYY-MM-DD.png

螺旋模擬包含:

  • 多情境 Interest/Tax Ratio 10年演變曲線
  • 風險區間背景(綠/黃/橙/紅)
  • 利息支出分解堆疊圖
  • 各情境最終風險評估

生成歷史趨勢分析圖表(NEW!):

# 完整歷史趨勢分析(2015-2025)
python scripts/generate_historical_trend.py --output-dir ../../output

# 自定義時間範圍
python scripts/generate_historical_trend.py --start-year 2018 --end-year 2025

輸出:output/japan_debt_trend_YYYYMMDD.png

歷史趨勢分析包含:

  • Interest/Tax Ratio 11年完整走勢(2015-2025)
  • 稅收、利息支出、債務存量趨勢
  • 隱含平均利率演變
  • 風險分級分布統計
  • 階段性特徵分析(下降期/疫情期/反彈期)

單獨測試數據抓取

python scripts/fetch_jgb_yields.py --tenor 10Y
python scripts/fetch_tic_holdings.py

</quick_start>

  1. 快速檢查 - 查看最新的利息/稅收比與殖利率狀態
  2. 完整分析 - 執行完整的財政壓力測試與風險評估
  3. 情境壓測 - 自定義利率衝擊情境進行壓力測試
  4. 生成圖表 - 生成視覺化 Dashboard(PNG 圖檔)
  5. 債務螺旋模擬 - 模擬多年累積效應,生成螺旋演變圖表
  6. 歷史趨勢分析 - 分析 2015-2025 年完整歷史趨勢(NEW!)
  7. 方法論學習 - 了解指標計算與風險分級邏輯

請選擇或直接提供分析參數。

Read the full file on GitHub · 390 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 390 lines · 74 tokens per session scan A 682f1567929a

Subscribe to this mod's changes

analyze-japan-debt-service-tax-burden is a skill published in the GitHub repository fatfingererr/macro-skills (3 stars, last pushed 7mo ago), licensed MIT. It adds 74 tokens to every session and 4,544 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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