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us-cpi-pce-comparator

Analyze CPI and PCE divergence with bucket-level attribution from FRED and BEA/BLS data.

3|1|Updated Jan 12, 2026
Search Tags:#inflation#analyze#PCE#CPI#FRED#BLS
One-click install
npx skills add https://github.com/fatfingererr/macro-skills --skill us-cpi-pce-comparator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: us-cpi-pce-comparator
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/us-cpi-pce-comparator
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill us-cpi-pce-comparator

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the comparison of CPI (fixed weights) and PCE (dynamic weights) to reveal divergence signals and identify low-volatility, high-weight buckets where PCE may re-accelerate, aiding Fed-focused inflation monitoring.

Core Features & Use Cases

  • Automated Headline vs. Core divergence: compute CPI vs PCE signals and gaps in bps.
  • Bucket-level attribution: highlight contributors and weight effects driving divergence.
  • Rapid monitoring: run quick checks or full analyses over selectable date ranges for ongoing surveillance.
  • Use Case: a macro strategist wants to assess if PCE is re-accelerating despite CPI cooling and plan trading or hedging decisions.

Quick Start

Run the quick-check workflow with: python scripts/cpi_pce_analyzer.py --quick or perform a full analysis with date range: python scripts/cpi_pce_analyzer.py --start 2020-01-01 --end 2024-12-01 --measure yoy

Frequently Asked Questions about us-cpi-pce-comparator

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

FAQPage Schema
How do I analyze CPI vs PCE divergence for inflation monitoring?▼

To analyze CPI versus PCE divergence, compare fixed-weight CPI against dynamic-weight PCE data to identify inflation signals and gaps in basis points. This process uses FRED and BLS data to pinpoint bucket-level attribution and potential Federal Reserve inflation risks.

Why does PCE re-accelerate while CPI is cooling?▼

PCE may re-accelerate while CPI cools due to dynamic weight adjustments in PCE versus fixed weights in CPI. This process identifies low-volatility, high-weight buckets driving divergence, helping macro strategists monitor Federal Reserve inflation risks.

Can I use Python pandas and requests to fetch BLS and FRED inflation data?▼

Yes, you can use Python with pandas, numpy, and requests to fetch and analyze BLS and FRED inflation data. This approach implements modular data fetchers and script-based analysis to deliver structured inflation comparison outputs.

How do I run a quick CPI and PCE comparison over a custom date range?▼

Run a quick CPI and PCE comparison by executing the analyzer script with the --quick flag for rapid checks, or specify custom start and end dates with the --measure yoy parameter for full historical divergence analysis.

What is the best way to attribute inflation divergence to specific buckets?▼

The best way to attribute inflation divergence is by calculating bucket-level weight effects and contributors using dynamic PCE and fixed CPI weights. This reveals specific components driving headline and core inflation gaps.

What are the limitations of using fixed-weight CPI for inflation risk monitoring?▼

A limitation of fixed-weight CPI is its inability to adjust to consumer substitution like dynamic-weight PCE, potentially masking re-acceleration in low-volatility buckets. Monitoring both metrics prevents missing Federal Reserve inflation risk signals.

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