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/scenario-analyzer

@2a3359f

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

Use this Skill: https://skilld.dev/gh/tradermonty/claude-trading-skills/scenario-analyzer

This session only. Nothing lands on disk.

SKILL.md

β‰ˆ119 tokens always: the name and description. β‰ˆ2.5k when used: this file. β‰ˆ6.6k more on demand in 3 files.

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

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                    Skill (orchestrator)                              β”‚
β”‚                                                                      β”‚
β”‚  Phase 1: Preparation                                                β”‚
β”‚  β”œβ”€ Headline parsing                                                 β”‚
β”‚  β”œβ”€ Event type classification                                        β”‚
β”‚  └─ Reference loading                                                β”‚
β”‚                                                                      β”‚
β”‚  Phase 2: Agent invocation                                           β”‚
β”‚  β”œβ”€ scenario-analyst (primary analysis)                              β”‚
β”‚  └─ strategy-reviewer (second opinion)                               β”‚
β”‚                                                                      β”‚
β”‚  Phase 3: Integration & report generation                            β”‚
β”‚  └─ reports/scenario_analysis_<topic>_YYYYMMDD.md                   β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

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 Γ— 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 β€” 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:

File Format Description
reports/scenario_analysis_<topic>_YYYYMMDD.md Markdown Comprehensive scenario analysis report

Output contents:

  • List of related news articles
  • 3 scenarios β€” 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?

Source: SKILL.md on GitHub

2 warnings16d5 checks Β· Risk SAFE
  • Gen Agent Trust Hub16d

    The skill is safe. It outlines an orchestrator workflow that uses specialized subagents to analyze investment scenarios based on news headlines. No malicious patterns, obfuscation, or unauthorized access vectors were detected.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: MEDIUM Β· 1 issue

  • Runlayer7mo

    4/4 files flagged

  • ZeroLeaks5mo

    Score: 93/100 Β· 2 sections analyzed

Signed by skilld at 2a3359f. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 19 hours ago.

Activeupdated 5 months ago

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