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tradermonty/scenario-analyzer

Skill that analyzes 18-month scenarios from a news headline. Runs the primary analysis with the scenario-analyst agent and obtains a second opinion with the strategy-reviewer agent. Generates a comprehensive English report covering 1st/2nd/3rd-order impacts, recommended stocks, and a critical review. Example: /scenario-analyzer "Fed raises rates by 50bp" Triggers: news analysis, scenario analysis, 18-month outlook, medium-to-long-term investment strategy

scenario-analyzer란 무엇인가요?

scenario-analyzer is a Claude Code agent skill that skill that analyzes 18-month scenarios from a news headline. Runs the primary analysis with the scenario-analyst agent and obtains a second opinion with the strategy-reviewer agent. Generates a comprehensive English report covering 1st/2nd/3rd-order impacts, recommended stocks, and a critical review. Example: /scenario-analyzer "Fed raises rates by 50bp" Triggers: news analysis, scenario analysis, 18-month outlook, medium-to-long-term investment strategy.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/tradermonty/claude-trading-skills/tree/main/skills/scenario-analyzer

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Scenario Analyzer

Overview

This skill analyzes medium-to-long-term (18-month) investment scenarios starting from a news headline. It invokes two specialized agents in sequence (scenario-analyst and strategy-reviewer) and integrates multi-angle analysis with a critical review into a comprehensive report.

When to Use This Skill

Use this skill when:

  • You want to analyze the medium-to-long-term investment impact of a news headline
  • You want to construct multiple 18-month scenarios
  • You want sector/stock impacts organized into 1st/2nd/3rd-order effects
  • You need a comprehensive analysis that includes a second opinion

Examples:

/scenario-analyzer "Fed raises interest rates by 50bp, signals more hikes ahead"
/scenario-analyzer "China announces new tariffs on US semiconductors"
/scenario-analyzer "OPEC+ agrees to cut oil production by 2 million barrels per day"

Prerequisites

  • API Keys: None (uses only WebSearch/WebFetch)
  • MCP Servers: None
  • Dependencies: The scenario-analyst and strategy-reviewer agents must be available via the Task tool

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                    Skill (orchestrator)                              │
│                                                                      │
│  Phase 1: Preparation                                                │
│  ├─ Headline parsing                                                 │
│  ├─ Event type classification                                        │
│  └─ Reference loading                                                │
│                                                                      │
│  Phase 2: Agent invocation                                           │
│  ├─ scenario-analyst (primary analysis)                              │
│  └─ strategy-reviewer (second opinion)                               │
│                                                                      │
│  Phase 3: Integration & report generation                            │
│  └─ reports/scenario_analysis_<topic>_YYYYMMDD.md                   │
└─────────────────────────────────────────────────────────────────────┘

Workflow

Phase 1: Preparation

Step 1.1: Headline Parsing

Parse the headline provided by the user.

  1. Headline check

    • Confirm a headline was passed as an argument
    • If not provided, ask the user for input
  2. Keyword extraction

    • Key entities (company names, country names, institution names)
    • Numeric data (rates, prices, quantities)
    • Actions (raise, cut, announce, agree, etc.)

Step 1.2: Event Type Classification

Classify the headline into one of the following categories:

CategoryExamples
Monetary PolicyFOMC, ECB, BOJ, rate hike, rate cut, QE/QT
GeopoliticsWar, sanctions, tariffs, trade friction
Regulation & PolicyEnvironmental regulation, financial regulation, antitrust
TechnologyAI, EV, renewables, semiconductors
CommoditiesCrude oil, gold, copper, agricultural products
Corporate & M&AAcquisitions, bankruptcies, earnings, industry restructuring

Step 1.3: Reference Loading

Based on the event type, load the relevant references:

Read references/headline_event_patterns.md
Read references/sector_sensitivity_matrix.md
Read references/scenario_playbooks.md

Reference contents:

  • headline_event_patterns.md: Historical event patterns and market reactions
  • sector_sensitivity_matrix.md: Event × sector impact-magnitude matrix
  • scenario_playbooks.md: Scenario-construction templates and best practices

Phase 2: Agent Invocation

Step 2.1: Invoke scenario-analyst

Use the Agent tool to invoke the primary analysis agent.

Agent tool:
- subagent_type: "scenario-analyst"
- prompt: |
    Perform an 18-month scenario analysis for the following headline.

    ## Target Headline
    [the input headline]

    ## Event Type
    [classification result]

    ## Reference Information
    [summary of the loaded references]

    ## Analysis Requirements
    1. Use WebSearch to collect related news from the past 2 weeks
    2. Construct 3 scenarios — Base/Bull/Bear (probabilities sum to 100%)
    3. Analyze 1st/2nd/3rd-order impacts by sector
    4. Select 3-5 positive- and 3-5 negative-impact stocks (US market only)
    5. Output everything in English

Expected output:

  • List of related news articles
  • Details of the 3 scenarios (Base/Bull/Bear)
  • Sector impact analysis (1st/2nd/3rd-order)
  • Stock recommendation list

Step 2.2: Invoke strategy-reviewer

Using the scenario-analyst's results, invoke the review agent.

Agent tool:
- subagent_type: "strategy-reviewer"
- prompt: |
    Review the following scenario analysis.

    ## Target Headline
    [the input headline]

    ## Analysis Result
    [the full scenario-analyst output]

    ## Review Requirements
    Review from the following angles:
    1. Overlooked sectors/stocks
    2. Validity of the scenario probability allocation
    3. Logical consistency of the impact analysis
    4. Detection of optimism/pessimism bias
    5. Proposal of alternative scenarios
    6. Realism of the timeline

    Output constructive and specific feedback in English.

Expected output:

  • Pointing out blind spots
  • Opinion on the scenario probabilities
  • Pointing out bias
  • Proposal of alternative scenarios
  • Final recommendations

Phase 3: Integration & Report Generation

Step 3.1: Integrate Results

Integrate the output of both agents to produce the final investment judgment.

Integration points:

  1. Fill in the blind spots raised in the review
  2. Adjust the probability allocation (if needed)
  3. Make the final judgment accounting for bias
  4. Formulate a concrete action plan

Step 3.2: Generate Report

Generate the final report in the following format and save it to a file.

Save location: reports/scenario_analysis_<topic>_YYYYMMDD.md

# Headline Scenario Analysis Report

**Analyzed at**: YYYY-MM-DD HH:MM
**Target headline**: [the input headline]
**Event type**: [classification category]

---

## 1. Related News Articles
[news list collected by scenario-analyst]

## 2. Scenario Overview (through 18 months out)

### Base Case (XX% probability)
[scenario details]

### Bull Case (XX% probability)
[scenario details]

### Bear Case (XX% probability)
[scenario details]

## 3. Sector / Industry Impact

### 1st-Order Impact (direct)
[impact table]

### 2nd-Order Impact (value chain / related industries)
[impact table]

### 3rd-Order Impact (macro / regulation / technology)
[impact table]

## 4. Stocks Expected to Benefit (3-5 tickers)
[stock table]

## 5. Stocks Expected to Be Hurt (3-5 tickers)
[stock table]

## 6. Second Opinion / Review
[strategy-reviewer output]

## 7. Final Investment Judgment & Implications

### Recommended Actions
[concrete actions informed by the review]

### Risk Factors
[list of key risks]

### Monitoring Points
[indicators / events to follow]

---
**Generated by**: scenario-analyzer skill
**Agents**: scenario-analyst, strategy-reviewer

Step 3.3: Save the Report

  1. Create the reports/ directory if it does not exist
  2. Save as scenario_analysis_<topic>_YYYYMMDD.md (e.g., scenario_analysis_venezuela_20260104.md)
  3. Notify the user that the save completed
  4. Do not save directly to the project root

Output

This skill generates the following file:

FileFormatDescription
reports/scenario_analysis_<topic>_YYYYMMDD.mdMarkdownComprehensive scenario analysis report

Output contents:

  • List of related news articles
  • 3 scenarios — Base/Bull/Bear (with probability allocation)
  • Sector impact analysis (1st/2nd/3rd-order)
  • Positive/negative stock recommendations
  • Second opinion / review
  • Final investment judgment & implications

Resources

References

  • references/headline_event_patterns.md - Event patterns and market reactions
  • references/sector_sensitivity_matrix.md - Sector sensitivity matrix
  • references/scenario_playbooks.md - Scenario-construction templates

Agents

  • scenario-analyst - Primary scenario analysis
  • strategy-reviewer - Second opinion / review

Important Notes

Language

  • All analysis and output are in English
  • Stock tickers remain in their standard (English) symbols

Target Market

  • Stock selection is US-listed equities only
  • ADRs included

Time Horizon

  • Scenarios target 18 months
  • Described in 3 phases: 0-6 months / 6-12 months / 12-18 months

Probability Allocation

  • Base + Bull + Bear = 100%
  • Each scenario's probability is described with its rationale

Second Opinion

  • Mandatory (always invoke strategy-reviewer)
  • Review results are reflected in the final judgment

Output Location (Important)

  • Always save under the reports/ directory
  • Path: reports/scenario_analysis_<topic>_YYYYMMDD.md
  • Example: reports/scenario_analysis_fed_rate_hike_20260104.md
  • Create the reports/ directory if it does not exist
  • Must not save directly to the project root

Quality Checklist

Confirm the following before finalizing the report:

  • Is the headline parsed correctly?
  • Is the event type classification appropriate?
  • Do the 3 scenario probabilities sum to 100%?
  • Are the 1st/2nd/3rd-order impacts logically connected?
  • Is the stock selection backed by concrete rationale?
  • Is the strategy-reviewer review included?
  • Is the final judgment reflecting the review documented?
  • Is the report saved to the correct path?

Individual skills in this repo

This repo contains 14 individual skills — each has its own dedicated page.

tradermonty/backtest-expert

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

tradermonty/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.

tradermonty/market-news-analyst

This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.

tradermonty/options-strategy-advisor

Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.

tradermonty/portfolio-manager

Comprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.

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.

tradermonty/signal-postmortem

Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog.

tradermonty/skill-designer

Design new Claude skills from structured idea specifications. Use when the skill auto-generation pipeline needs to produce a Claude CLI prompt that creates a complete skill directory (SKILL.md, references, scripts, tests) following repository conventions.

tradermonty/skill-idea-miner

Mine Claude Code session logs for skill idea candidates. Use when running the weekly skill generation pipeline to extract, score, and backlog new skill ideas from recent coding sessions.

tradermonty/theme-detector

Detect and analyze trending market themes across sectors. Use when user asks about current market themes, trending sectors, sector rotation, thematic investing, what themes are hot or cold, or wants to identify bullish and bearish market narratives with lifecycle analysis.

tradermonty/uptrend-analyzer

Analyzes market breadth using Monty's Uptrend Ratio Dashboard data to diagnose the current market environment. Generates a 0-100 composite score from 5 components (breadth, sector participation, rotation, momentum, historical context). Use when asking about market breadth, uptrend ratios, or whether the market environment supports equity exposure. No API key required.

tradermonty/us-stock-analysis

Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.

tradermonty/value-dividend-screener

Screen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals.

tradermonty/vcp-screener

Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path. Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points; in historical single-ticker mode walks a multi-year history and emits every VCP that formed with forward-outcome stats (breakout / stop-hit / timeout). Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, Stage 2 momentum stock scanning, or historical VCP pattern study on a specific ticker (e.g. FIX, TSLA).

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