sector-analyst

Name: sector-analyst
Availability: InStock
Author: dnkrow

Rank sector uptrend ratios and compute cyclical-versus-defensive risk regime scores.

1|Updated May 15, 2026

Search Tags:#python #sector rotation #market cycle #csv analysis #risk regime #quantitative ranking #overbought oversold

Ready to run

npx skills add https://github.com/dnkrow/skill --skill sector-analyst-dnkrow

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sector-analyst
Source: https://github.com/dnkrow/skill/tree/main/claude-global/sector-analyst
Command: npx skills add https://github.com/dnkrow/skill --skill sector-analyst-dnkrow

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you identify which market sectors are currently leading or lagging and translate those relative signals into a probabilistic market cycle phase view.

Core Features & Use Cases

Sector Rotation Ranking: Ranks sectors by uptrend ratio (trend and status included).
Risk Regime Scoring: Computes cyclical vs defensive regime score to assess risk-on vs risk-off conditions.
Overbought/Oversold Flags: Highlights sectors whose uptrend ratios are above or below predefined thresholds.
Market Cycle Phase Estimation: Estimates likely cycle phase (early/mid/late/recession) from leader/laggard patterns and trend alignment.
Data Recency Awareness: Checks the freshness of the underlying public timeseries data and warns if stale.
Outputs for Decision Work: Produces both human-readable Markdown and machine-readable JSON, optionally saving reports.

Quick Start

Use the sector-analyst skill to generate a sector rotation report for the latest public CSV data by running it with: python3 scripts/analyze_sector_rotation.py.

Frequently Asked Questions about sector-analyst

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

FAQPage Schema

How do I identify market cycle phases using sector rotation data?▼

Market cycle phases are identified by ranking sector uptrend ratios and evaluating leader/laggard trend alignment. This analysis estimates whether the market is in an early, mid, late, or recession phase based on cyclical versus defensive sector performance.

How do I generate a sector rotation report from public CSV files?▼

Generate a sector rotation report by executing a Python script that fetches public CSV timeseries data. The script ranks sector uptrend ratios, calculates risk regime scores, and outputs formatted Markdown or JSON reports.

Does sector rotation analysis require a specific Python environment setup?▼

Sector rotation analysis requires a standard Python execution environment to run the analysis scripts. It automatically fetches public CSV inputs from GitHub sources, requiring no external API keys or paid data subscriptions.

Can I get machine-readable JSON outputs for quantitative sector ranking?▼

Yes, quantitative sector ranking supports machine-readable JSON outputs alongside human-readable Markdown. The analysis computes risk regime scores and overbought/oversold flags, optionally saving both report formats locally.

How do I detect overbought and oversold sectors in a market cycle?▼

Detect overbought and oversold sectors by evaluating their uptrend ratios against predefined thresholds. This analysis flags specific sectors extending beyond normal risk parameters during the current market cycle phase.

What happens if my public CSV timeseries data is stale?▼

When public CSV timeseries data is stale, the analysis includes data recency awareness checks that warn you. This ensures sector rotation rankings and market cycle phase estimations do not rely on outdated inputs.