market-response-modeler — Claude Code command: setup and usage
Simulates market and buyer behavior when launching products, updating prices, or countering competitor moves. Useful before rolling out major business changes.
npx claude-code-templates@latest --command simulation/market-response-modelerWhat market-response-modeler does
This command runs market simulations based on specific business triggers. It accepts arguments such as --product-launch, --pricing-change, --marketing-campaign, or --competitive-response. The prompt breaks down customer segments and maps out expected competitor actions.
Behind the scenes, the model uses game theory and Monte Carlo calculations. It relies on Read, Write, Edit, and WebSearch tools. Web search permissions are necessary. Without internet access, the assistant cannot gather fresh market signals, which leaves the analysis purely theoretical.
What it helps with
- predict buyer reactions to price updates
- simulate competitor moves after a product launch
- evaluate marketing campaign scenarios
- optimize launch timing and distribution channels
Example prompts for the agent
- /market-response-modeler --pricing-change raise enterprise tier by 20%
- /market-response-modeler --product-launch launch new analytics add-on
- /market-response-modeler --competitive-response price cuts from primary competitor
How to install market-response-modeler
1claude-code-templates
npx claude-code-templates@latest --command simulation/market-response-modeler2Manually: put the files in place
.claude/commands/market-response-modeler.mdCall it with /market-response-modeler. For all projects use ~/.claude/commands/
Manually: put the files in place
~/.codex/prompts/market-response-modeler.mdCall it with /prompts:market-response-modeler. Codex marks this format deprecated in favour of skills
Manually: put the files in place
.github/prompts/market-response-modeler.prompt.mdManually: put the files in place
.gemini/commands/market-response-modeler.tomlGemini stores commands as TOML: the prompt text goes into the prompt field
Questions about market-response-modeler
How to install market-response-modeler in Claude Code?
Run npx claude-code-templates@latest --command simulation/market-response-modeler in your terminal. This adds the command configuration to your project.
What market triggers does this command support?
You can pass --product-launch, --pricing-change, --marketing-campaign, or --competitive-response as command flags to test different scenarios.
What tools does market-response-modeler require?
The configuration relies on Read, Write, Edit, and WebSearch. Internet access is needed to pull current competitive details.
Source text
market-response-modeler.md · MIT
--- allowed-tools: Read, Write, Edit, WebSearch argument-hint: "[market-trigger] | --product-launch | --pricing-change | --marketing-campaign | --competitive-response" description: Model comprehensive market and customer responses with segment analysis, behavioral prediction, and optimization --- # Market Response Modeler Model comprehensive market and customer responses with advanced behavioral prediction: **$ARGUMENTS** ## Current Market Context - Market definition: Based on $ARGUMENTS (target segments, geographic scope, competitive landscape) - Response trigger: Product launch, pricing change, marketing campaign, or competitive response - Available data: Customer behavior data, market research, and historical response patterns - Success metrics: Key performance indicators for measuring response effectiveness ## Task Create comprehensive market response simulation with predictive analytics and optimization: **Market Trigger**: Use $ARGUMENTS to model responses to product launches, pricing changes, marketing campaigns, or competitive actions **Response Framework**: 1. **Market Segmentation** - Comprehensive segment analysis with behavioral, demographic, and needs-based categorization 2. **Response Behavior Modeling** - Customer journey mapping, response driver analysis, and intensity prediction 3. **Competitive Response Integration** - Competitor reaction modeling and market dynamic effects 4. **Response Simulation Engine** - Multi-scenario testing with timeline modeling and probability assessment 5. **Prediction Algorithms** - Statistical modeling, machine learning, and expert system integration 6. **Response Optimization** - Message, offering, channel, and timing optimization strategies **Advanced Analytics**: Monte Carlo simulations, competitive game theory, behavioral economics integration, and real-time calibration. **Decision Support**: Strategic recommendations with segment-specific tactics, risk mitigation, and success measurement frameworks. **Output**: Complete market response prediction with segment analysis, optimization recommendations, competitive scenarios, and implementation guidelines for maximum market impact.
© davila7 · MIT · davila7/claude-code-templates