decision-tree-explorer — Claude Code command: features and setup
Maps complex decision trees with probability scoring, expected value calculations, and risk modeling. Useful for sizing investments, operational pivots, or crisis responses.
npx claude-code-templates@latest --command simulation/decision-tree-explorerWhat decision-tree-explorer does
The command structures multi-path dilemmas into systematic decision trees. It outlines alternative paths, estimates probabilities via base rates, and computes expected values across financial and strategic dimensions. Sensitivity checks and break-even testing verify critical assumptions.
Advanced evaluation covers Monte Carlo simulations and real options valuation. The final report delivers weighted outcomes, risk mitigations, and immediate implementation steps tied to specific success metrics.
The workflow uses the WebSearch tool alongside Read, Write, and Edit to fetch external benchmarks. Without defined constraints or evaluation criteria in your prompt, probability calculations will require manual refinement.
What it helps with
- Calculate expected values across competing investment options
- Map contingency strategies for emergency crisis responses
- Test critical assumptions via sensitivity and break-even analysis
- Run Monte Carlo simulations to rank strategic project options
Example prompts for the agent
- /decision-tree-explorer Evaluate building in-house vs buying ready-made enterprise software --investment
- /decision-tree-explorer Database provider outage response and failover options --crisis-response
- /decision-tree-explorer Entering the European enterprise market next quarter --strategic
How to install decision-tree-explorer
1claude-code-templates
npx claude-code-templates@latest --command simulation/decision-tree-explorer2Manually: put the files in place
.claude/commands/decision-tree-explorer.mdCall it with /decision-tree-explorer. For all projects use ~/.claude/commands/
Manually: put the files in place
~/.codex/prompts/decision-tree-explorer.mdCall it with /prompts:decision-tree-explorer. Codex marks this format deprecated in favour of skills
Manually: put the files in place
.github/prompts/decision-tree-explorer.prompt.mdManually: put the files in place
.gemini/commands/decision-tree-explorer.tomlGemini stores commands as TOML: the prompt text goes into the prompt field
Questions about decision-tree-explorer
How to install decision-tree-explorer in Claude Code?
Run npx claude-code-templates@latest --command simulation/decision-tree-explorer in your terminal. This command writes the markdown configuration to .claude/commands/decision-tree-explorer.md.
What argument flags does decision-tree-explorer accept?
You can supply free-text decision context along with --strategic, --investment, --operational, or --crisis-response flags to narrow the evaluation scope.
Which tools does this command require?
The configuration specifies Read, Write, Edit, and WebSearch. WebSearch allows Claude to pull live market data and relevant base rates for probability estimation.
Source text
decision-tree-explorer.md · MIT
--- allowed-tools: Read, Write, Edit, WebSearch argument-hint: "[decision-context] | --strategic | --investment | --operational | --crisis-response" description: Explore complex decision branches with probability analysis, expected value calculation, and optimization --- # Decision Tree Explorer Explore complex decision scenarios with comprehensive probability analysis and optimization: **$ARGUMENTS** ## Current Decision Context - Decision scope: Based on $ARGUMENTS (strategic, investment, operational, crisis response) - Available options: Current alternatives under consideration - Success criteria: Key metrics for decision evaluation - Resource constraints: Limitations affecting available choices ## Task Create comprehensive decision tree analysis for optimal choice selection: **Decision Context**: Use $ARGUMENTS to analyze strategic decisions, investments, operations, or crisis responses **Decision Framework**: 1. **Option Generation** - Comprehensive alternative identification including hybrid and innovative approaches 2. **Probability Assessment** - Systematic likelihood estimation using base rates, expert judgment, and market data 3. **Expected Value Analysis** - Multi-dimensional value calculation including financial, strategic, and risk factors 4. **Sensitivity Analysis** - Critical assumption testing and break-even analysis 5. **Risk Assessment** - Comprehensive risk identification, impact analysis, and mitigation strategies 6. **Optimization Engine** - Multi-criteria decision analysis with stakeholder preference integration **Advanced Analytics**: Monte Carlo simulations, real options valuation, decision path optimization, and robustness testing. **Implementation Integration**: Connect analysis to specific actions, success metrics, and contingency planning. **Output**: Complete decision tree with probability-weighted outcomes, expected value calculations, risk assessments, and strategic recommendations with implementation guidance.
© davila7 · MIT · davila7/claude-code-templates
