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macro-regime-detector

Detect structural macro regime transitions from monthly cross-asset ratio signals.

1|Updated May 15, 2026
Search Tags:#macro regime detection#cross-asset ratios#equity-bond correlation#credit conditions#yield curve analysis#portfolio posture#financial modeling prep
One-click install
npx skills add https://github.com/dnkrow/skill --skill macro-regime-detector-dnkrow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: macro-regime-detector
Source: https://github.com/dnkrow/skill/tree/main/claude-global/macro-regime-detector
Command: npx skills add https://github.com/dnkrow/skill --skill macro-regime-detector-dnkrow

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Investors often struggle to translate evolving cross-asset signals (equities, credit, rates, and sector leadership) into a coherent view of structural macro regime transitions over the next 1–2 years.

Core Features & Use Cases

  • Cross-asset ratio regime detection: Scores transition strength using monthly-frequency ratios to identify shifts among Concentration, Broadening, Contraction, Inflationary, and Transitional states.
  • 6 weighted signal components with evidence: Evaluates market concentration (RSP/SPY), yield curve regime (10Y-2Y spread or SHY/TLT proxy), credit conditions (HYG/LQD), size factor (IWM/SPY), equity-bond relationship (SPY/TLT plus correlation), and sector rotation (XLY/XLP).
  • Actionable reporting: Produces a JSON report for programmatic use and a Markdown report that summarizes regime evidence, consistency checks, confidence, transition probability, and portfolio posture guidance.

Quick Start

Ask the Claude Code runtime to run macro-regime-detector on the latest available data using your FMP API key, then read the generated Markdown report for the current regime, transition likelihood, and recommended posture.

Frequently Asked Questions about macro-regime-detector

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect macro regime transitions using cross-asset ratios?▼

Cross-asset ratio signals across market concentration (RSP/SPY), yield curve (10Y-2Y spread or SHY/TLT proxy), credit conditions (HYG/LQD), size factor (IWM/SPY), equity-bond relationship (SPY/TLT), and sector rotation (XLY/XLP) are weighted to detect structural macro regime transitions over a 1–2 year horizon.

What is the best way to analyze equity-bond correlation for portfolio positioning?▼

Evaluating the SPY/TLT relationship alongside yield curve, credit conditions, and sector rotation signals generates a composite score, regime classification, and confidence level to guide strategic portfolio posture over a 1–2 year horizon.

How do I run macro regime detection using a Financial Modeling Prep API key?▼

Executing the main Python orchestrator with an FMP API key retrieves rate-limited historical data, applies a SHY/TLT proxy fallback if needed, and generates JSON and Markdown reports containing regime evidence, confidence, transition probability, and portfolio posture guidance.

Can I use yield curve analysis for strategic portfolio positioning rather than short-term tactical moves?▼

Yes, yield curve analysis assesses whether regime leadership is shifting over a 1–2 year horizon specifically for strategic portfolio positioning rather than short-term tactical moves.

What data sources does cross-asset regime detection use when FMP API rate limits are hit?▼

When FMP API rate limits are hit, the system uses a SHY/TLT proxy fallback for yield curve data while continuing to evaluate six weighted signal components including market concentration, credit conditions, size factor, and sector rotation.

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