Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vivy-yi/finance-skills --skill macro-forward-adjustmentgit clone --depth 1 https://github.com/vivy-yi/finance-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vivy-yi/finance-skills/macro-forward-adjustment)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/macro-forward-adjustment"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/macro-forward-adjustment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/macro-forward-adjustment"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/macro-forward-adjustment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00107 | $0.01827 |
| Opus 5 | $0.00053 | $0.00914 |
| Sonnet 5 | $0.00021 | $0.00365 |
| Haiku 4.5 | $0.00011 | $0.00183 |
Grade A, and why
macro-forward-adjustment 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 9d ago.
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.
What it actually says
加载上下文
首次使用时: 读取 ../../CLAUDE.md 获取宏观因子定义/情景权重/调整方法。
/macro-forward-adjustment — 宏观前瞻调整
第一步:确定宏观因子与情景
□ 宏观因子选择(来自 CLAUDE.md):
□ 因子 1:GDP 增速 — 基准 [X]%,乐观 [+X]%,悲观 [-X]%
□ 因子 2:[房价指数 / 行业指数] — 基准 [X]%,乐观 [+X]%,悲观 [-X]%
□ 因子 3:[失业率 / 利率] — 基准 [X]%,乐观 [-X]%,悲观 [+X]%
□ 宏观数据来源:
□ GDP:[国家统计局 / IMF / 世界银行]
□ 房价:[国家统计局 / 中指院]
□ 行业违约率:[穆迪 / 标普 / 行业协会]
□ 情景权重(来自 CLAUDE.md):
□ 乐观情景:[X]%
□ 基准情景:[X]%
□ 悲观情景:[X]%
□ 权重合计:[X]% — [✅ = 100% / ⚠️ 须调整]
第二步:映射宏观因子至 PD / LGD
□ 宏观因子 → PD 调整系数:
□ GDP +1% → PD 下降 [X]%(宏观改善)
□ GDP -1% → PD 上升 [X]%(宏观恶化)
□ 计算每个情景下的调整后 PD:
□ 乐观情景:PD_adj = PD_base × (1 - [X]% × ΔGDP_乐观)
□ 基准情景:PD_adj = PD_base × (1 - [X]% × ΔGDP_基准)
□ 悲观情景:PD_adj = PD_base × (1 - [X]% × ΔGDP_悲观)
□ 宏观调整 PD 敏感性:
□ GDP +1%:PD 下降约 [X]%
□ GDP -1%:PD 上升约 [X]%
□ 对 Stage 1 / Stage 2 / Stage 3 分别应用(Stage 3 PD=100% 不调整)
第三步:计算三情景 ECL
□ 乐观情景 ECL:
□ Stage 1 ECL(乐观):[X] 万元
□ Stage 2 ECL(乐观):[X] 万元
□ Stage 3 ECL(乐观):[X] 万元(不调整)
□ 乐观 ECL 合计:[X] 万元
□ 基准情景 ECL:
□ 基准 ECL 合计:[X] 万元(来自 ecl-calculation 结果)
□ 悲观情景 ECL:
□ Stage 1 ECL(悲观):[X] 万元
□ Stage 2 ECL(悲观):[X] 万元
□ Stage 3 ECL(悲观):[X] 万元(不调整)
□ 悲观 ECL 合计:[X] 万元
□ 三情景对比:
□ 乐观 [X] 万 → 基准 [X] 万 → 悲观 [X] 万
□ 悲观 vs 乐观差距:[X] 万元 — [✅ 差距合理 / ⚠️ 差距过大]
第四步:加权 ECL 计算
□ 加权 ECL 计算:
□ 加权 ECL = 乐观 ECL × [X]% + 基准 ECL × [X]% + 悲观 ECL × [X]%
□ 计算:
□ = [X]万 × [X]% + [X]万 × [X]% + [X]万 × [X]%
□ = [X]万元
□ 前瞻调整影响:
□ 调整前 ECL(基准):[X] 万元
□ 调整后 ECL(加权):[X] 万元
□ 净调整:[±X] 万元 / [±X]%
□ 调整方向:[✅ 增加 ECL(悲观权重高)/ ⚠️ 减少 ECL(乐观权重高)]
第五步:合理性验证
□ 与市场共识对比:
□ 基准 GDP 假设 [X]% vs 央行预测 [X]% — [✅ 一致 / ⚠️ 偏离]
□ 房价假设 [X]% vs 市场一致预期 [X]% — [✅ 一致 / ⚠️ 偏离]
□ 与同业对比(如可得):
□ 同业前瞻调整幅度:[±X]% — [✅ 在合理范围 / ⚠️ 偏离较大]
□ 避免的错误:
□ 🔴 零前瞻调整(IFRS 9 明确禁止)
□ 🔴 仅用历史数据无前瞻
□ 🔴 单一宏观因子
□ 🔴 情景权重主观随意
□ 敏感性分析:
□ 悲观情景权重 +10% → ECL 增加 [X] 万元
□ GDP 假设下调 1% → ECL 增加 [X] 万元
输出格式
═══════════════════════════════════════
宏观前瞻调整报告
评估截止日:[YYYY-MM-DD]
═══════════════════════════════════════
【宏观因子与情景】
| 因子 | 乐观 | 基准 | 悲观 |
|------------|--------|--------|--------|
| GDP 增速 | [+X]% | [X]% | [-X]% |
| 房价指数 | [+X]% | [X]% | [-X]% |
| [行业指数] | [+X]% | [X]% | [-X]% |
【三情景 ECL 结果】
| 情景 | 权重 | Stage 1 | Stage 2 | Stage 3 | 合计 |
|--------|-------|---------|---------|--------|--------|
| 乐观 | [X]% | [X]万 | [X]万 | [X]万 | [X]万 |
| 基准 | [X]% | [X]万 | [X]万 | [X]万 | [X]万 |
| 悲观 | [X]% | [X]万 | [X]万 | [X]万 | [X]万 |
【加权 ECL】
□ 调整前 ECL(基准):[X] 万元
□ 调整后 ECL(加权):[X] 万元
□ 前瞻调整影响:[±X] 万元 / [±X]%
【合理性验证】
□ GDP 假设 vs 央行预测:[✅ 一致 / ⚠️ 偏离]
□ 房价假设 vs 市场预期:[✅ 一致 / ⚠️ 偏离]
□ 同业对比:[✅ 在合理范围 / ⚠️ 偏离较大]
【敏感性分析】
□ 悲观权重 +10% → ECL 增加 [X] 万元
□ GDP -1% → ECL 增加 [X] 万元
═══════════════════════════════════════
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.
- 9d ago First seen · 175 lines · 107 tokens per session scan A 9b0839661070
macro-forward-adjustment is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 107 tokens to every session and 1,827 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
wealth-structure
A framework for thinking about wealth through ownership, responsibility, leverage, and avoiding risks that could end your progress.
alpha-vantage
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market…
hedgefundmonitor
Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity…
edgartools
Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K…
asc-ppp-pricing
Set territory-specific pricing for subscriptions and in-app purchases using current asc setup, pricing summary, price import, and price schedule commands. Use when adjusting prices by country or implementing localized PPP strategies.
fred-economic-data
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring…