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sector-analyst

Fetch TraderMonty's public sector CSV data to rank uptrends and estimate cycle phase and risk regime.

1|Updated Apr 6, 2026
Search Tags:#finance#data-analysis#market-cycle#sector-rotation#uptrend#csv-data#cyclical-defensive
One-click install
npx skills add https://github.com/kavi-lin/stock --skill sector-analyst-kavi-lin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sector-analyst
Source: https://github.com/kavi-lin/stock/tree/main/skills/sector-analyst
Command: npx skills add https://github.com/kavi-lin/stock --skill sector-analyst-kavi-lin

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Analyzes sector rotation and market-cycle positioning by ingesting public CSV data to provide sector leadership insights, risk regime, and cycle phase estimates.

Core Features & Use Cases

  • Rank sectors by uptrend ratio to identify leaders and laggards.
  • Quantify cyclical vs defensive risk regime and estimate current market phase.
  • Identify overbought/oversold sectors and provide scenario-based positioning guidance.
  • Optional: analyze industry-level detail from chart inputs to supplement data-driven conclusions.

Quick Start

Analyze the latest sector rotation CSV to generate a sector rotation report.

Frequently Asked Questions about sector-analyst

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

FAQPage Schema
How do I analyze sector rotation data to identify market cycle phases?▼

No external API keys are required. The Skill fetches sector uptrend data from TraderMonty's public CSV dataset and runs on Python 3.8+ with standard libraries, making it accessible without complex financial data API configurations.

How do I rank sectors by uptrend strength using CSV data?▼

You can rank sectors by uptrend ratio by ingesting the public CSV dataset to identify market leaders and laggards. It also identifies overbought or oversold sectors to provide scenario-based positioning guidance for your portfolio.

Can I use chart images to supplement CSV market cycle analysis?▼

Yes, you can optionally analyze industry-level detail from chart image inputs. This supplements the data-driven conclusions from the CSV dataset to provide a more comprehensive sector rotation and risk regime report.

What is the difference between cyclical and defensive risk regimes in sector analysis?▼

Cyclical and defensive risk regimes quantify market posture by measuring sector leadership. Analyzing the CSV data distinguishes cyclical growth sectors from defensive sectors, allowing you to estimate the current market cycle phase and adjust positioning accordingly.

Does sector rotation analysis require external API keys or paid financial data?▼

No external API keys are required. The Skill fetches sector uptrend data from TraderMonty's public CSV dataset and runs on Python 3.8+ with standard libraries, making it accessible without complex financial data API configurations.

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