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 duolongworld/AI_Renaissance --skill layer0_trackinggit clone --depth 1 https://github.com/duolongworld/AI_RenaissanceWrote 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/duolongworld/ai_renaissance/layer0_tracking)<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/layer0_tracking"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/layer0_tracking.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.02488 |
| Opus 5 | $0.00019 | $0.01244 |
| Sonnet 5 | $0.00008 | $0.00498 |
| Haiku 4.5 | $0.00004 | $0.00249 |
Grade A, and why
layer0_tracking 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 8d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Layer 0: 双经济体追踪与中美交互通道
执行方式
- 数值计算（Agent直接调用
scripts/analyzer.py，不需要LLM） - 智能分析（Agent读取本SKILL.md作为LLM提示词）
- 混合模式（数值计算 + LLM智能判断）
说明：本层需要 LLM 进行智能判断，主要用于：
- 评估两国的政策宽松/收紧程度（需要解读政策信号）
- 判断传导通道的触发状态（需要综合多因素判断）
- 判定中美交互层次（需要定性分析）
适用范围
追踪中国与美国两大经济体的宏观状态，识别中美交互通道的触发状态，为后续7层流水线提供输入。
输入数据规范
必填数据
| 数据项 | 来源 | 字段名 | 说明 |
|---|---|---|---|
| 中国统计局制造业PMI | NBS | nbs_manufacturing_pmi | 月频 |
| 中国财新制造业PMI | Caixin | caixin_manufacturing_pmi | 月频 |
| 中国CPI同比 | NBS | cpi_yoy | 月频 |
| 中国PPI同比 | NBS | ppi_yoy | 月频 |
| 1Y LPR | PBOC | 1y_lpr | 月频 |
| 5Y LPR | PBOC | 5y_lpr | 月频 |
| DR007 | CFETS | dr007 | 日频 |
| 社融存量同比 | PBOC | tsf_yoy | 月频 |
| 10Y国债收益率 | CFETS | cn_10y_yield | 日频 |
| A股ERP | Wind | csi300_erp | 日频 |
| 美国ISM制造业PMI | ISM | us_ism_pmi | 月频 |
| 美国非农就业 | BLS | nonfarm_payrolls | 月频 |
| 美国PCE同比 | BEA | pce_yoy | 月频 |
| 美国核心PCE | BEA | core_pce_yoy | 月频 |
| 美联储政策利率 | Fed | ffr | 日频 |
| SOFR | NY Fed | sofr | 日频 |
| 10Y美债收益率 | Bloomberg | us_10y_yield | 日频 |
| 美元指数DXY | Bloomberg | dxy_index | 日频 |
| USD/CNH | HKMA | usd_cnh | 日频 |
可选数据
| 数据项 | 来源 | 用途 |
|---|---|---|
| 中国MLF利率 | PBOC | 政策维度评估 |
| 中国降准幅度 | PBOC | 政策维度评估 |
| 美联储总资产 | Fed | 流动性维度 |
| VIX恐慌指数 | Bloomberg | 风险偏好评估 |
| 中美10Y利差 | 自算 | 传导通道1 |
| 美国IG/HY利差 | ICE BofA | 信用风险 |
分析步骤
Step 1: 抓取数据
从 data_sources/ 获取中美两国五大维度的最新数据。
Step 2: 计算z-score
对每个指标计算5年滚动z-score标准化（已由 Layer 0 analyzer 内部处理）。
Step 3: 计算跨国指标
- 中美10Y利差 = 中国10Y国债收益率 - 美国10Y国债收益率
- 美元指数（DXY）相对强弱
- USD/CNH 汇率水平
Step 4: 检查6条传导通道
根据各通道的触发条件，判断通道是否触发及传导强度：
| 通道 | 起点变量 | 中介变量 | 终点资产 | 触发条件 |
|---|---|---|---|---|
| 1 | 中美10Y利差 | 北向资金 | A股核心资产 | 利差变化超过0.5σ |
| 2 | 美元指数DXY | 原油/铜/南华 | A股周期股 | DXY变化超过1.0σ |
| 3 | 美国实际利率 | VIX/美元流动性 | 港股/成长股 | 实际利率变化超过1.0σ |
| 4 | 中国社融脉冲 | 铁矿石/铜价 | 全球周期资产 | 社融脉冲变化超过1.0σ |
| 5 | 全球PMI | 韩国/越南出口 | 中国出口/制造业股 | 全球PMI变化超过0.5σ |
| 6 | 地缘政治事件 | VIX/CNH波动率 | 港股/CNH/半导体 | 地缘事件评分超过阈值 |
Step 5: 判定交互层次
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.
- 8d ago First seen · 172 lines · 38 tokens per session scan A 1fba05b75279
layer0_tracking is a skill published in the GitHub repository duolongworld/AI_Renaissance (59 stars, last pushed 11d ago), licensed Apache-2.0. It adds 38 tokens to every session and 2,488 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 2026-08-30.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.