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Scenario Analyzer

CommunityPopular
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

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill namescenario-analyzer
Stars
2.8K
Forks
647
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the Scenario Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/scenario-analyzer .claude/skills/scenario-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scenario Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scenario Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scenario Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

markdown
# 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?

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Scenario Analyzer AI skill do?

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

Why use Scenario Analyzer on TypingMind?

Because you install it once and use it with any model. Scenario Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scenario Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/scenario-analyzer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scenario Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scenario Analyzer?

As many as you like. As long as a model supports skills, you can use Scenario Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scenario Analyzer AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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