Scenario Analyzer

Scenario Analyzer

Overview

This skill takes a news headline and builds a structured 18-month investment scenario from it. It chains two specialised subagents — scenario-analyst (primary multi-scenario analyst) and strategy-reviewer (critical second opinion) — and integrates their outputs into a single report covering scenarios, sector impacts, stock picks, and a reviewer-informed final view.

When to Use This Skill

Use this skill when:

• The user wants to think through the medium-term investment impact of a news event
• Multiple 18-month scenarios (Base / Bull / Bear) need to be constructed
• Sector and stock impacts need to be organised by 1st/2nd/3rd-order linkages
• A second opinion / red-team pass is wanted alongside the primary analysis

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 WebSearch / WebFetch only)
• MCP servers: none
• Dependencies: the scenario-analyst and strategy-reviewer agents must be available via the Agent tool

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                    Skill (orchestrator)                              │
│                                                                      │
│  Phase 1: Preparation                                                │
│  ├─ Headline parsing                                                 │
│  ├─ Event-type classification                                        │
│  └─ Reference loading                                                │
│                                                                      │
│  Phase 2: Subagent calls                                             │
│  ├─ scenario-analyst (primary analysis)                              │
│  └─ strategy-reviewer (second opinion)                               │
│                                                                      │
│  Phase 3: Integration and report generation                          │
│  └─ reports/scenario_analysis__YYYYMMDD.md                    │
└─────────────────────────────────────────────────────────────────────┘

Workflow

Phase 1: Preparation

Step 1.1: Parse the headline

Examine the user-provided headline:

1. Confirm the headline

• If a headline is supplied as an argument, use it.
• If not, prompt the user for one.

1. Extract keywords

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

Step 1.2: Classify the event type

Map the headline to one of the categories below:

| Category | Examples | |---------|-----| | Monetary policy | FOMC, ECB, BoJ, rate hike/cut, QE/QT | | Geopolitics | War, sanctions, tariffs, trade frictions | | Regulation / policy | Environmental, financial, antitrust | | Technology | AI, EV, renewables, semiconductors | | Commodities | Oil, gold, copper, agriculture | | Corporate / M&A | Acquisitions, bankruptcies, earnings, industry restructuring |

Step 1.3: Load references

Based on the event type, read 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 matrix
• scenario_playbooks.md: Templates and best practices for building scenarios

---

Phase 2: Subagent calls

Step 2.1: Call scenario-analyst

Invoke the primary analysis agent via the Agent tool:

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

    ## Target headline
    [the user's headline]

    ## Event type
    [classification result]

    ## Reference notes
    [summary of the references that were loaded]

    ## Requirements
    1. Use WebSearch to collect relevant news from the past two weeks.
    2. Build 3 scenarios (Base / Bull / Bear) with probabilities summing to 100%.
    3. Analyse 1st / 2nd / 3rd-order impacts by sector.
    4. Recommend 3–5 stocks each for positive and negative exposure (US-listed only).
    5. Output entirely in English.

Expected outputs:

• A list of relevant news articles
• Three scenarios (Base / Bull / Bear) in detail
• Sector impact analysis (1st / 2nd / 3rd order)
• Stock recommendation list

Step 2.2: Call strategy-reviewer

Feed the scenario-analyst output into the reviewer agent:

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

    ## Target headline
    [the user's headline]

    ## Primary analysis
    [full output from scenario-analyst]

    ## Review requirements
    Critique the analysis along these axes:
    1. Missed sectors / stocks
    2. Reasonableness of scenario probability allocation
    3. Logical consistency of impact analysis
    4. Detection of optimism / pessimism bias
    5. Alternative scenarios to consider
    6. Timeline realism

    Output constructive, specific feedback in English.

Expected outputs:

• Identified blind spots
• Comments on scenario probabilities
• Bias call-outs
• Alternative scenario proposals
• Final recommendations

---

Phase 3: Integration and report generation

Step 3.1: Integrate the two analyses

Combine the agents' outputs into a final investment view:

Integration points:

1. Fill in gaps that the reviewer identified.
2. Adjust probability allocation if warranted.
3. Reflect reviewer's bias warnings in the final view.
4. Lay out a concrete action plan.

Step 3.2: Generate the report

Write the final report to reports/scenario_analysis__YYYYMMDD.md:

# Headline Scenario Analysis Report

**Analysis date:** YYYY-MM-DD HH:MM
**Headline:** [user input]
**Event type:** [classification]

---

## 1. Related News
[news list from scenario-analyst]

## 2. Scenarios (18-month outlook)

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

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

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

## 3. Sector / Industry Impact

### 1st-order impact (direct)
[impact table]

### 2nd-order impact (value chain / adjacent industries)
[impact table]

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

## 4. Positively Impacted Stocks (3–5)
[stock table]

## 5. Negatively Impacted Stocks (3–5)
[stock table]

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

## 7. Final Investment View

### Recommended actions
[reviewer-informed concrete actions]

### Key risks
[principal risks]

### Monitoring points
[indicators and events to track]

---
**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__YYYYMMDD.md (e.g. scenario_analysis_venezuela_20260104.md).
3. Notify the user that the report has been saved.
4. Never write the report to the project root.

---

Output

This skill produces the following file:

| File | Format | Description | |---------|------|------| | reports/scenario_analysis__YYYYMMDD.md | Markdown | Integrated scenario analysis report |

Contents:

• Related news list
• Base / Bull / Bear scenarios with probability allocation
• Sector impact analysis (1st / 2nd / 3rd order)
• Positive / negative stock recommendations
• Second-opinion review
• Final investment view

Resources

References

• references/headline_event_patterns.md — Event patterns and historical 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 is in English.
• Stock tickers stay in their native (English) form.

Target market

• Stock recommendations are limited to US-listed instruments (ADRs included).

Time horizon

• Scenarios cover 18 months, broken into 0–6 / 6–12 / 12–18-month phases.

Probability allocation

• Base + Bull + Bear must sum to 100%.
• Each scenario's probability must be justified.

Second opinion

• The reviewer agent is always invoked — the second opinion is not optional.
• Reviewer findings must be reflected in the final view.

Output location (important)

• The report must be saved under reports/.
• Path: reports/scenario_analysis__YYYYMMDD.md
• Example: reports/scenario_analysis_fed_rate_hike_20260104.md
• Create reports/ if it does not exist.
• Never write the report directly to the project root.

---

Quality Checklist

Before finalising the report, confirm:

• [ ] The headline has been parsed correctly.
• [ ] The event-type classification is appropriate.
• [ ] The three scenario probabilities sum to 100%.
• [ ] 1st / 2nd / 3rd-order impacts connect logically.
• [ ] Stock picks are backed by specific reasoning.
• [ ] The strategy-reviewer output is included.
• [ ] The final view reflects the reviewer's findings.
• [ ] The report has been saved to the correct path.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: xonevn-ai
• Source: xonevn-ai/xone-trading-skills
• License: MIT
• Homepage: https://xone.vn

Install and usage instructions live in the source repository linked above.