# Scenario Analyzer > | - **Type:** Skill - **Install:** `agentstack add skill-xonevn-ai-xone-trading-skills-scenario-analyzer` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [xonevn-ai](https://agentstack.voostack.com/s/xonevn-ai) - **Installs:** 0 - **Category:** [Finance & Payments](https://agentstack.voostack.com/c/finance-and-payments) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [xonevn-ai](https://github.com/xonevn-ai) - **Source:** https://github.com/xonevn-ai/xone-trading-skills/tree/main/skills/scenario-analyzer - **Website:** https://xone.vn ## Install ```sh agentstack add skill-xonevn-ai-xone-trading-skills-scenario-analyzer ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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. 2. **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`: ```markdown # 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](https://github.com/xonevn-ai) - **Source:** [xonevn-ai/xone-trading-skills](https://github.com/xonevn-ai/xone-trading-skills) - **License:** MIT - **Homepage:** https://xone.vn Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-xonevn-ai-xone-trading-skills-scenario-analyzer - Seller: https://agentstack.voostack.com/s/xonevn-ai - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.