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The skill appears to implement the advertised macro-regime analysis, but there are clear mismatches between its metadata/instructions and the files (missing declared env var, undeclared dependencies, and a path mismatch), so you should review before installing or running code.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Einstein Research — Macro Regime Detector」。简介：Detect structural macro regime transitions (1-2 year horizon) using cross-asset…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/clawdiri-ai/einstein-research-regime-dv/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
id: 'einstein-research-regime'
name: 'einstein-research-regime'
description: 'Detect structural macro regime transitions (1-2 year horizon) using cross-asset
  ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions,
  size factor, equity-bond relationship, and sector rotation to identify regime shifts
  between Concentration, Broadening, Contraction, Inflationary, and Transitional
  states. Run when user asks about macro regime, market regime change, structural
  rotation, or long-term market positioning.'
version: '1.0.0'
author: 'DaVinci'
last_amended_at: null
trigger_patterns: []
pre_conditions:
  git_repo_required: false
  tools_available: []
expected_output_format: 'natural_language'
---

# Macro Regime Detector

Detect structural macro regime transitions using monthly-frequency cross-asset ratio analysis. This skill identifies 1-2 year regime shifts that inform strategic portfolio positioning.

## When to Use

- User asks about current macro regime or regime transitions
- User wants to understand structural market rotations (concentration vs broadening)
- User asks about long-term positioning based on yield curve, credit, or cross-asset signals
- User references RSP/SPY ratio, IWM/SPY, HYG/LQD, or other cross-asset ratios
- User wants to assess whether a regime change is underway

## Workflow

1. Load reference documents for methodology context:
   - `references/regime_detection_methodology.md`
   - `references/indicator_interpretation_guide.md`

2. Execute the main analysis script:
   ```bash
   python3 skills/macro-regime-detector/scripts/macro_regime_detector.py
   ```
   This fetches 600 days of data for 9 ETFs + Treasury rates (10 API calls total).

3. Read the generated Markdown report and present findings to user.

4. Provide additional context using `references/historical_regimes.md` when user asks about historical parallels.

## Prerequisites

- **FMP API Key** (required): Set `FMP_API_KEY` environment variable or pass `--api-key`
- Free tier (250 calls/day) is sufficient (script uses ~10 calls)

## 6 Components

| # | Component | Ratio/Data | Weight | What It Detects |
|---|-----------|------------|--------|-----------------|
| 1 | Market Concentration | RSP/SPY | 25% | Mega-cap concentration vs market broadening |
| 2 | Yield Curve | 10Y-2Y spread | 20% | Interest rate cycle transitions |
| 3 | Credit Conditions | HYG/LQD | 15% | Credit cycle risk appetite |
| 4 | Size Factor | IWM/SPY | 15% | Small vs large cap rotation |
| 5 | Equity-Bond | SPY/TLT + correlation | 15% | Stock-bond relationship regime |
| 6 | Sector Rotation | XLY/XLP | 10% | Cyclical vs defensive appetite |

## 5 Regime Classifications

- **Concentration**: Mega-cap leadership, narrow market
- **Broadening**: Expanding participation, small-cap/value rotation
- **Contraction**: Credit tightening, defensive rotation, risk-off
- **Inflationary**: Positive stock-bond correlation, traditional hedging fails
- **Transitional**: Multiple signals but unclear pattern

## Output

- `macro_regime_YYYY-MM-DD_HHMMSS.json` — Structured data for programmatic use
- `macro_regime_YYYY-MM-DD_HHMMSS.md` — Human-readable report with:
  1. Current Regime Assessment
  2. Transition Signal Dashboard
  3. Component Details
  4. Regime Classification Evidence
  5. Portfolio Posture Recommendations

## Relationship to Other Skills

| Aspect | Macro Regime Detector | Market Top Detector | Market Breadth Analyzer |
|--------|----------------------|--------------------|-----------------------|
| Time Horizon | 1-2 years (structural) | 2-8 weeks (tactical) | Current snapshot |
| Data Granularity | Monthly (6M/12M SMA) | Daily (25 business days) | Daily CSV |
| Detection Target | Regime transitions | 10-20% corrections | Breadth health score |
| API Calls | ~10 | ~33 | 0 (Free CSV) |

## Script Arguments

```bash
python3 macro_regime_detector.py [options]

Options:
  --api-key KEY       FMP API key (default: $FMP_API_KEY)
  --output-dir DIR    Output directory (default: current directory)
  --days N            Days of history to fetch (default: 600)
```