scenario-planning
When the user wants to analyze supply chain scenarios, perform risk analysis, evaluate what-if scenarios, or build contingency plans. Also use when the user mentions "scenario analysis," "what-if planning," "risk scenarios," "contingency planning," "monte carlo simulation," "sensitivity analysis," "stress testing," or "disruption planning." For demand uncertainty, see demand-forecasting. For S&OP scenario integration, see sales-operations-planning.
Profile →npx skills add majiayu000/claude-skill-registry --skill scenario-planning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
You are an expert in supply chain scenario planning and risk analysis. Your goal is to help organizations evaluate alternative futures, assess risks, develop contingency plans, and make robust decisions under uncertainty.
How it works
- Performs an initial assessment to gather planning context, business environment, scope, objectives, and data/resources.
- Provides a Scenario Planning Framework with types of analysis: Sensitivity Analysis, What-If Scenarios, Monte Carlo Simulation, Strategic Scenarios, and Stress Testing.
- Outlines a Scenario Development Process with Phase 1 Define Scope & Objectives, Phase 2 Identify Key Uncertainties (external and internal), Phase 3 Define Scenarios (Driver-Based, Event-Based, Monte Carlo), Phase 4 Model Scenarios (build base model, apply changes, simulate over time, compute metrics, compare scenarios).
- Includes concrete modeling constructs and code snippets that illustrate state representation (SupplyChainState), scenario management (ScenarioModel), and basic evaluation/effects on inventory, service level, and costs.
- Provides a Monte Carlo Simulation section describing probabilistic analysis, process steps, and a Python-based implementation outline for running simulations, generating demand, costs, supply reliability, and calculating outcomes like total_cost and service_level.
When to use it
Use when the user wants to analyze supply chain scenarios, perform risk analysis, evaluate what-if scenarios, or build contingency plans. Also apply when user mentions terms like "scenario analysis," "what-if planning," "risk scenarios," "contingency planning," "monte carlo simulation," "sensitivity analysis," "stress testing," or "disruption planning."
What it can touch
- Tools: claude-code
- Data structures: Python code blocks define dataclasses (SupplyChainState), and classes (ScenarioModel, MonteCarloScenarioAnalysis) used to model and analyze scenarios.
- Operations include defining base state, adding scenarios, evaluating scenarios over time, and performing Monte Carlo simulations with distributions for demand, costs, and supply reliability.
Caveats
- License: MIT
- The skill includes example Python code snippets and may require adaptation to specific environments; ensure dependencies (numpy, pandas, matplotlib, scipy) are available.
- No promises of specific outcomes; described methods are for modeling and comparison, not guaranteed results.
# Scenario Planning You are an expert in supply chain scenario planning and risk analysis. Your goal is to help organizations evaluate alternative futures, assess risks, develop contingency plans, and make robust decisions under uncertainty. ## Initial Assessment Before building scenario plans, understand: 1. **Planning Context** - What decisions need to be made? - What uncertainties or risks are most concerning? - Planning horizon? (short-term tactical vs. long-term strategic) - Current planning process and tools? 2. **Business Environment** - Key market uncertainties? (demand, supply, pricing) - External risks? (geopolitical, natural disasters, economic) - Internal vulnerabilities? (single-source suppliers, capacity constraints) - Historical disruptions experienced? 3. **Scope & Objectives** - What part of supply chain? (end-to-end, specific segment) - Quantitative analysis or qualitative scenarios? - One-time analysis or ongoing process? - Decision context? (investment, strategy, operations) 4. **Data & Resources** - Historical variability data available? - Cost and performance data? - Risk databases or incident logs? - Modeling tools and capabilities? --- ## Scenario Planning F
- Initial Assessment
- Scenario Planning Framework
- Types of Scenario Analysis
- Scenario Development Process
- Phase 1: Define Scope & Objectives
- Phase 2: Identify Key Uncertainties
- Phase 3: Define Scenarios
- Phase 4: Model Scenarios
- Monte Carlo Simulation
- Probabilistic Scenario Analysis
- Sensitivity Analysis
- One-Way Sensitivity
- Multi-Way Sensitivity (Tornado Diagram)
- Risk Assessment & Quantification
What does the scenario-planning skill do?
When the user wants to analyze supply chain scenarios, perform risk analysis, evaluate what-if scenarios, or build contingency plans. Also use when the user mentions "scenario analysis," "what-if planning," "risk scenarios," "contingency planning," "monte carlo simulation," "sensitivity analysis," "stress testing," or "disruption planning." For demand uncertainty, see demand-forecasting. For S&OP scenario integration, see sales-operations-planning.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scenario-planning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.