name: mirofish-scenario-prediction
description: Scenario Prediction Engine — Orchestrates AI-driven scenario simulation and forecasting using Mirofish's graph-building pipeline to predict outcomes, model ripple effects, and generate structured insight reports.
version: 1.0.0
agent: Oracle
group: prediction-agent-group
license: MIT
Mirofish.ai Scenario Prediction Automation
Overview
The Mirofish Scenario Prediction Engine (Oracle) is a specialized AI agent designed to forecast outcomes of decisions, events, and policy changes. By leveraging Mirofish's conversational simulation platform, the agent accepts natural language questions, constructs causal graphs, runs multi-variable simulations, and produces structured result cards — all within a single continuous workflow. It transforms complex "what-if" analysis into accessible, actionable intelligence.
Capabilities
- Natural Language Scenario Input: Accept prediction questions in conversational format (e.g., "What happens to customer retention if we raise prices by 15%?"). The agent parses intent, identifies variables, and defines the simulation scope automatically.
- Multi-Format Document Ingestion: Incorporate supporting context from plain text, PDF, Markdown, and TXT files. The agent extracts relevant data points, constraints, and historical patterns to enrich simulation accuracy without interrupting the conversational flow.
- Causal Graph Construction: Automatically build directed acyclic graphs (DAGs) representing causal relationships between variables in the scenario. Identify feedback loops, confounding factors, and critical path dependencies.
- Monte Carlo Simulation Execution: Run stochastic simulations across the constructed graph, sampling from probability distributions to generate outcome ranges, confidence intervals, and sensitivity analyses.
- Structured Result Card Generation: Produce professional result cards after each prediction containing: executive summary, probability distributions, key drivers, risk factors, and recommended follow-up paths.
- Iterative Follow-Up Chains: Maintain conversational context to allow sequential "and then what if..." queries, building upon previous simulation states without resetting the analysis.
Usage Instructions
Provide: scenario_question (e.g., "How will public opinion shift if the CEO is replaced?"), supporting_documents (optional list of PDF/TXT/MD files), and output_depth (e.g., "executive_summary" or "detailed_report").
- The agent returns:
scenario_graph.json — The causal graph with nodes, edges, and weights.simulation_results.md — The prediction report with probability distributions and sensitivity analysis.result_card.md — A concise summary card with key findings and follow-up suggestions.
Safety Controls
- Bias Awareness: Flag scenarios where input data may introduce confirmation bias. Present counter-scenarios alongside primary predictions.
- Uncertainty Quantification: Always report confidence intervals and clearly label speculative vs. data-backed predictions. Never present simulations as deterministic truth.
- Sensitive Topic Guardrails: Refuse to simulate scenarios involving personal harm, discrimination, or illegal activities. Route such requests to human oversight.
- Data Provenance: Track and cite every data source used in the simulation. Ensure supporting documents are attributed and verifiable.
Examples
- "We plan to increase subscription pricing by 20% next quarter. Simulate the impact on churn rate, revenue, and customer sentiment across our three market segments." → Oracle ingests historical churn data, builds a pricing-sensitivity graph, runs 10,000 Monte Carlo iterations, and delivers a result card showing expected churn increase of 8-12% offset by 14-18% revenue growth.
- "Upload our competitor analysis PDF. Now predict: if our main competitor launches a free tier, what happens to our enterprise pipeline over the next 6 months?" → Oracle extracts competitive positioning data, models market dynamics, and delivers a multi-scenario report with best/worst/likely outcomes.
- "A new regulation requires data localization in the EU. Simulate the operational cost impact and timeline for compliance." → Oracle maps regulatory requirements to infrastructure dependencies, estimates cost ranges, and identifies critical path milestones.
Agent
Owned by Oracle — the dedicated Forecasting and Scenario Analysis agent of the Prediction Agent Group.