macro_top_down_analysis

Classify macro regimes and match historical analogs for cross-asset allocation.

Updated Mar 20, 2026

Search Tags:#analysis #macro #historical-analogs #macro-data #asset-allocation #regime-classification

Ready to run

npx skills add https://github.com/Eveyz/agentskills --skill macro-top-down-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: macro_top_down_analysis
Source: https://github.com/Eveyz/agentskills/tree/main/macro_top_down_analysis
Command: npx skills add https://github.com/Eveyz/agentskills --skill macro-top-down-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze the global macro environment and translate recent data releases into evidence-based asset-allocation insights with citations.

Core Features & Use Cases

Regime classification and regime-driven asset-allocation guidance based on inflation, growth, and labor indicators.
Historical-analog matching to contextualize current macro conditions and potential asset performance.
Cross-asset implications spanning equities, bonds, commodities, and USD with transparent sourcing.

Quick Start

Provide a current macro snapshot by analyzing the latest inflation, rates, growth, and employment data.

Frequently Asked Questions about macro_top_down_analysis

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

FAQPage Schema

How do I analyze macro data for cross-asset allocation guidance?▼

Macro data analysis for cross-asset allocation applies real-time inflation, rates, growth, and employment data to classify regimes and return actionable asset signals. It translates recent data releases into evidence-based allocation insights with transparent sourcing and confidence scores.

What is regime classification in macroeconomic analysis?▼

Regime classification in macroeconomic analysis categorizes the current market environment using inflation, growth, and labor indicators. It informs cross-asset allocation by matching current conditions with historical analogs to contextualize potential asset performance.

How do I use historical analogs to contextualize current macro conditions?▼

Using historical analogs to contextualize current macro conditions involves matching real-time inflation, rates, growth, and employment data with past market regimes. This process projects potential asset performance across equities, bonds, commodities, and USD.

Can I get cross-asset allocation signals for equities, bonds, and commodities?▼

Cross-asset allocation signals for equities, bonds, commodities, and USD are generated by applying regime classification to current macro data. The process outputs a strict JSON payload containing evidence-based allocation guidance, transparent sources, and a confidence score.

What is the best way to translate real-time inflation and rates data into asset signals?▼

The best way to translate real-time inflation and rates data into asset signals is applying top-down macro analysis with regime classification. This approach evaluates economic indicators against historical analogs to produce a strict JSON payload with cross-asset implications and confidence scores.

Does macro top-down analysis output structured data for automated trading workflows?▼

Macro top-down analysis outputs structured data for automated trading workflows by returning a strict JSON payload conforming to a provided schema. This payload includes regime classification, historical analogs, cross-asset implications, transparent sources, and a confidence score.