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Einstein Research — Macro Regime Detector
by
RunByDaVinci
· GitHub ↗
· v0.1.0
· MIT-0
108
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Install in OpenClaw
/install einstein-research-regime-dv
Description
Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions...
README (SKILL.md)
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
-
Load reference documents for methodology context:
references/regime_detection_methodology.mdreferences/indicator_interpretation_guide.md
-
Execute the main analysis script:
python3 skills/macro-regime-detector/scripts/macro_regime_detector.pyThis fetches 600 days of data for 9 ETFs + Treasury rates (10 API calls total).
-
Read the generated Markdown report and present findings to user.
-
Provide additional context using
references/historical_regimes.mdwhen user asks about historical parallels.
Prerequisites
- FMP API Key (required): Set
FMP_API_KEYenvironment 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 usemacro_regime_YYYY-MM-DD_HHMMSS.md— Human-readable report with:- Current Regime Assessment
- Transition Signal Dashboard
- Component Details
- Regime Classification Evidence
- 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
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)
Usage Guidance
This skill appears to implement the advertised macro-regime detector, but there are a few inconsistencies you should address before running it: (1) SKILL.md and README require an FMP API key (FMP_API_KEY) but the registry metadata does not declare any required env vars — treat any API key as sensitive and only provide one with least privilege; (2) there is no install spec or dependency manifest even though the code needs Python packages (pandas, yfinance) — run inside an isolated environment (venv/container) and install deps explicitly; (3) SKILL.md references a different script path than the repository contains — confirm the correct run command; (4) because the skill includes executable Python files, review scripts/fmp_client.py and the main macro_regime_detector.py to confirm network calls are only to expected data providers (FMP/Yahoo) and that no unexpected endpoints or credential reads exist; (5) run the code in a sandboxed environment with a throwaway API key or the minimal-permission key first. If you are not comfortable doing these checks, ask the publisher for clarification or request a version with a clear dependency manifest and explicit, matching metadata.
Capability Analysis
Type: OpenClaw Skill
Name: einstein-research-regime-dv
Version: 0.1.0
The 'einstein-research-regime-dv' skill bundle is a legitimate financial analysis tool designed to detect macro-economic regime shifts using cross-asset ratio analysis. The code, primarily located in 'scripts/macro_regime_detector.py' and its associated calculator modules, fetches historical ETF and Treasury data from the Financial Modeling Prep (FMP) API and performs quantitative analysis (moving averages, crossovers, and correlations). The behavior is entirely consistent with the documentation in 'SKILL.md' and 'README.md', and the bundle includes a comprehensive test suite. No indicators of data exfiltration, malicious execution, or prompt injection were found.
Capability Assessment
Purpose & Capability
The code and documentation implement a cross-asset macro-regime detector that matches the name/description: calculators for RSP/SPY, IWM/SPY, HYG/LQD, SPY/TLT, yield curve and sector rotation are present and weighted as described. This capability legitimately needs market data API access (FMP or Yahoo) and Python data libraries.
Instruction Scope
SKILL.md instructs the agent to load local reference docs and to execute a Python script that will fetch ~600 days of market data (network calls). The run command in SKILL.md references 'skills/macro-regime-detector/scripts/macro_regime_detector.py' but the repository shows 'scripts/macro_regime_detector.py' (path mismatch). The instructions require an API key (or optional Yahoo fallback) and to read local reference files; they do not ask to read unrelated system files or credentials, but the agent will execute third-party Python code from the skill bundle which can perform arbitrary actions unless sandboxed.
Install Mechanism
There is no install spec (instruction-only from a platform perspective) but the bundle includes ~23 Python files. README mentions installing packages (yfinance, pandas) but these dependencies are not declared in registry metadata or an install step. Running the scripts will require Python packages and will write output files (JSON/MD) to disk; absence of an explicit install step or dependency manifest increases operational risk (agent may fail or execute with missing/older libs).
Credentials
SKILL.md and README state an FMP API key is required (FMP_API_KEY env var or --api-key) though the registry metadata lists no required env vars / primary credential — a clear mismatch. Requesting an API key for a market-data service is proportionate to the stated purpose, but the missing declaration in metadata is an integrity issue. No other secrets are requested in code/README, and the code appears focused on fetching market data rather than exfiltrating arbitrary secrets.
Persistence & Privilege
The skill does not request always:true and does not declare system-wide config path changes. It is user-invocable and allows autonomous invocation (platform default) — nothing in the files shows it modifies other skills or requests elevated agent privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install einstein-research-regime-dv - After installation, invoke the skill by name or use
/einstein-research-regime-dv - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release
Metadata
Frequently Asked Questions
What is Einstein Research — Macro Regime Detector?
Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions... It is an AI Agent Skill for Claude Code / OpenClaw, with 108 downloads so far.
How do I install Einstein Research — Macro Regime Detector?
Run "/install einstein-research-regime-dv" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Einstein Research — Macro Regime Detector free?
Yes, Einstein Research — Macro Regime Detector is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Einstein Research — Macro Regime Detector support?
Einstein Research — Macro Regime Detector is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Einstein Research — Macro Regime Detector?
It is built and maintained by RunByDaVinci (@clawdiri-ai); the current version is v0.1.0.
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