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

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. 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.

2. When to Use

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"

3. 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

4. Quick Start 

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

5. 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:

Category Examples
Monetary Policy FOMC, ECB, BOJ, rate hike, rate cut, QE/QT
Geopolitics War, sanctions, tariffs, trade friction
Regulation & Policy Environmental regulation, financial regulation, antitrust
Technology AI, EV, renewables, semiconductors
Commodities Crude oil, gold, copper, agricultural products
Corporate & M&A Acquisitions, 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

```markdown

Headline Scenario Analysis Report

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

6. Resources

References:

  • skills/scenario-analyzer/references/headline_event_patterns.md
  • skills/scenario-analyzer/references/scenario_playbooks.md
  • skills/scenario-analyzer/references/sector_sensitivity_matrix.md