Agent skill · data analytics · tradermonty
sector-analyst
This skill should be used when analyzing sector rotation patterns and market cycle positioning. It fetches sector uptrend data from CSV (no API key required) and optionally accepts chart images for supplementary analysis. Use this skill when the user requests sector rotation analysis, cyclical vs defensive assessment, overbought/oversold identification, or market cycle phase estimation. All analysis and output are conducted in English.
Why this skill is useful
Adds specific analysis scripts for sector rotation and market cycle positioning that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
Sector Analyst Overview This skill enables comprehensive analysis of sector rotation and market cycle positioning by fetching uptrend ratio data from TraderMonty's public CSV dataset. It ranks sectors, calculates cyclical vs defensive risk regime scores, identifies overbought/oversold conditions, and estimates the current market cycle phase. Chart images can optionally supplement the data-driven analysis with industry-level detail. When to Use This Skill Use this skill when: User requests sector rotation analysis (no chart images required) User asks about cyclical vs defensive positioning User wants to know which sectors are overbought or oversold User requests market cycle phase estimation User provides sector performance charts for supplementary analysis User asks for sector-based scenario analysis or predictions Example user requests: "Run a sector rotation analysis" "Which sectors are leading — cyclical or defensive?" "Are any sectors overbought right now?" "What phase of the market cycle are we in?" "Analyze these sector performance charts and tell me where we are in the market cycle" Prerequisites Python 3.9+; no third-party libraries required (CSV fetched via stdlib urllib) No API keys required — data is fetched from a public GitHub repository Optional: Sector performance chart images for supplementary analysis Data Source Sector uptrend ratios are fetched from TraderMonty's public GitHub repository (no API key required): Sector Summary: sectorsummary.csv — uptrend ratio, trend, slope, and status per sector Freshness Check: uptrendratiotimeseries.csv — max(date) used to verify data recency Running the Script Analysis Workflow Follow this structured workflow: Step 1: CSV Data Collection 1. Run the analysis script: python3 scripts/analyzesectorrotation.py 2. Extract from the output: Sector ranking by uptrend ratio Risk regime (cyclical vs defensive) and score Overbought/oversold sectors Cycle phase estimate and confidence level 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/sector-analyst--f2e8b6