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 seaworld008/Commonly-used-high-value-skills --skill macro-regime-monitorgit clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-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/seaworld008/commonly-used-high-value-skills/macro-regime-monitor)<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/macro-regime-monitor"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/macro-regime-monitor/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/seaworld008/commonly-used-high-value-skills/macro-regime-monitor"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/macro-regime-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 26 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00038 | $0.02003 |
| Opus 5 | $0.00019 | $0.01001 |
| Sonnet 5 | $0.00008 | $0.00401 |
| Haiku 4.5 | $0.00004 | $0.00200 |
Grade A, and why
macro-regime-monitor 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Macro Regime Monitor (宏观周期监控器)
从宏观噪音中提取信号，识别当前的宏观范式（Macro Regime），为资产配置和行业轮动提供决策依据。本技能通过整合通胀、增长、利差和流动性等多维指标，帮助投资者判断市场正处于何种周期阶段，从而决定是“Risk-on”还是“Risk-off”。
安装与前提条件
# 确保已安装宏观数据抓取与分析库
pip install pandas numpy openbb requests
# 下载宏观分类脚本
npx clawhub install macro-regime-monitor
触发条件 / When to Use
- 每周宏观周报 (Weekly Macro Note)：在周一开盘前，梳理上周所有宏观数据的变化及其对周期的影响。
- 资产配置会议 (Asset-allocation Meetings)：在决定增配股票还是债权前，先确认宏观大环境。
- 风险模式切换 (Risk-on vs Risk-off Framing)：当突发地缘政治冲突或重大利率决议时，快速评估环境切换风险。
- 跨资产仓位更新 (Cross-asset Positioning)：根据宏观象限的变化，动态调整大宗商品、外汇及权益类资产的比例。
- 核心逻辑修正：当通胀数据连续三个月超预期，触发“通胀粘性”范式确认。
核心能力 / Core Capabilities
1. 核心指标实时跟踪 (Key Indicators Tracking)
- 操作步骤：
- 获取最新的 增长 (Growth) 数据：如 GDP, PMI (制造/服务), 零售销售。
- 获取最新的 通胀 (Inflation) 数据：如 CPI, PPI, PCE, 薪资增速。
- 获取最新的 流动性 (Liquidity) 数据：如 M2, 央行资产负债表变化, 逆回购（RRP）规模。
- 获取最新的 利差 (Spreads) 数据：如 10Y-2Y 收益率曲线倒挂程度, 信用违约掉期（CDS）。
- 最佳实践：优先关注“二阶导数”（即增长是否在放缓，或通胀是否在加速）。
2. 宏观象限分类 (Regime Classification)
- 操作步骤：
- 运行
scripts/classify_regime.py。 - 将当前环境映射到经典象限：
- 金发姑娘 (Goldilocks): 增长高，通胀低。
- 通货膨胀 (Reflation): 增长高，通胀高。
- 滞胀 (Stagflation): 增长低，通胀高。
- 衰退/通缩 (Recession): 增长低，通胀低。
- 运行
- 最佳实践：不仅给出标签，更要标注当前位置在象限中的偏移趋势。
3. 资产配置含义推导 (Allocation Implications)
- 操作步骤：
- 基于当前的 Regime，自动匹配历史胜率最高的资产类别。
- 生成“战术性资产配置”（TAA）建议。
- 识别最受影响的行业板块（如：滞胀环境下通常利好能源和必需消费品）。
- 最佳实践：提供“反转触发点”（Flip Points），例如：“如果 10Y 美债收益率突破 5.0%，则从成长股切换到防守型价值股”。
4. 情绪与头寸分析 (Sentiment & Positioning)
- 操作步骤：
- 抓取
Fear & Greed Index或AAII Sentiment Survey。 - 分析
Commitment of Traders (COT)报告，查看机构在大宗商品和外汇上的多空头寸分布。
- 抓取
常用命令/模板 / Common Patterns
宏观监控数据 JSON 模板 (Macro Indicators JSON)
{
"region": "US",
"data_points": {
"pmi_composite": 52.5,
"cpi_yoy": 3.4,
"fed_funds_rate": 5.25,
"yield_curve_10y2y": -0.45,
"m2_growth": 0.02
},
"trend": "Growth decelerating, Inflation sticky"
}
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed 00c60df37247
- 9d ago First seen · 137 lines · 38 tokens per session scan A 973c01699952
macro-regime-monitor is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (70 stars, last pushed 5d ago), licensed MIT. It adds 38 tokens to every session and 2,003 once invoked, about $0.0002 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.
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