Agent skill

market-environment-analysis

Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.

BaggaT236github.com/BaggaT236GitHub ↗
claude-codeships scriptsMIT
Install
npx skills add BaggaT236/AI-Trading-Skills --skill market-environment-analysis --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 5 KB
Bundled scripts: yes
Path: skills/market-environment-analysis/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 118
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Market Environment Analysis Comprehensive analysis tool for understanding market conditions and creating professional market reports anytime. ## When to Use - When you need a comprehensive overview of global market conditions - Before making trading or investment decisions - For daily/weekly market briefings - When assessing risk-on/risk-off sentiment - For understanding inter-market correlations and sector rotation - When preparing market reports for clients or personal records ## Prerequisites - **WebSearch access**: Required for fetching real-time market data - **No API keys required**: This skill uses web search for data collection - **Optional**: Economic calendar data for event-driven analysis ## Core Workflow ### 1. Initial Data Collection Collect latest market data using web_search tool: 1. Major stock indices (S&P 500, NASDAQ, Dow, Nikkei 225, Shanghai Composite, Hang Seng) 2. Forex rates (USD/JPY, EUR/USD, major currency pairs) 3. Commodity prices (WTI crude, Gold, Silver) 4. US Treasury yields (2-year, 10-year, 30-year) 5. VIX index (Fear gauge) 6. Market trading status (open/close/current values) ### 2. Market Environment Assessment Evaluate the following from collect

What's inside
Steps it walks through
  1. When to Use
  2. Prerequisites
  3. Core Workflow
  4. 1. Initial Data Collection
  5. 2. Market Environment Assessment
  6. 3. Report Structure
  7. Script Usage
  8. marketutils.py
  9. Reference Documentation
  10. Key Indicators Interpretation
  11. Analysis Patterns
  12. Output Examples
  13. Quick Summary Version
  14. Detailed Analysis Version
Ships with 5 files
  • references/analysis_patterns.md
  • references/indicators.md
  • scripts/market_utils.py
  • scripts/tests/__init__.py
  • scripts/tests/test_market_utils.py
Commands it runs
Generate report header
python scripts/market_utils.py
Available functions:
More from AI-Trading-Skills
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About this skill
What does the market-environment-analysis skill do?

Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.

How do I install it?

Run `npx skills add BaggaT236/AI-Trading-Skills --skill market-environment-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going