mirofish-skill

>

# MiroFish — AI Simulation Chat for Scenario Prediction

MiroFish is an open-source AI simulation engine that rehearses the future by spawning personas with distinct incentives, biases, and memory, then letting them interact across social surfaces over multiple rounds. Unlike single-model Q&A, it produces emergent dynamics you can't script.

> **Live at**: [mirofish.homes](https://mirofish.homes/)  
> **Source**: [github.com/666ghj/MiroFish](https://github.com/666ghj/MiroFish)

## When to Use This Skill

- **Campaign pressure testing** — how will different audiences amplify, resist, or reinterpret a message?
- **Policy impact analysis** — which stakeholders react first, and how does the cascade unfold?
- **Market reaction modeling** — simulate competitor responses before real-world deployment
- **PR crisis rehearsal** — identify the persona that triggers the first negative cascade
- **Product launch scenarios** — test positioning against multiple buyer personas simultaneously
- **Counterfactual exploration** — "What if we changed this one variable?"

## When Not to Use

- Simple factual Q&A (use standard LLM chat)
- Single-perspective analysis where interaction dynamics don't matter
- Real-time data analysis requiring live API feeds

## How It Works

### The Five-Stage Creative Process

1. **Seed the world**  
   Describe the scenario in plain language. Attach a strategy memo, policy brief, or market note for grounding. No structured input required — just like briefing a team.

2. **Map the dynamics**  
   The engine extracts actors, relationships, pressures, and factual anchors into a knowledge graph — the cast and conflict map before simulation begins.

3. **Run the rehearsal**  
   AI personas — each with distinct incentives, biases, and memory — interact across social surfaces over multiple rounds. Personas respond to each other, not just the initial prompt, producing emergent dynamics.

4. **Read the report**  
   A structured result card surfaces:
   - Most likely trajectory with confidence indicators
   - Risk signals and early warning flags
   - Narrative path analysis
   - Natural follow-up questions

5. **Keep directing**  
   Unlike a static forecast, continue questioning the simulation. Change variables. Test counterfactuals. Explore the world you created.

### Technical Architecture

| Component | Technology |
|-----------|-----------|
| Multi-agent orchestration | Custom persona spawning with memory persistence |
| Knowledge extraction | Graph-based actor/relationship mapping |
| Interaction surface | Chat interface with round-based progression |
| Deployment | Web application at [mirofish.homes](https://mirofish.homes/) |

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| Scenario description | Yes | Plain-language description of the situation to simulate |
| Supporting documents | No | Strategy briefs, policy documents, or market notes for factual grounding |
| Persona definitions | No | Custom stakeholder profiles (defaults to auto-generated) |

## Outputs

| Output | Description |
|--------|-------------|
| Trajectory forecast | Most likely outcome path with confidence score |
| Risk signals | Early warning indicators ranked by severity |
| Narrative paths | How different stakeholder narratives evolve over rounds |
| Follow-up questions | AI-generated deep-dive prompts for further exploration |

## Installation

### Web Access
Visit [mirofish.homes](https://mirofish.homes/) — no installation required.

### Self-Hosted (Open Source)
```bash
git clone https://github.com/666ghj/MiroFish.git
cd MiroFish
# Follow setup instructions in README
```

## Examples

### Example 1: Product Launch Scenario
```
Input: "We're launching a premium-priced AI writing tool. 
How will freelance writers, content agencies, and enterprise teams react?"

Output:
- Freelance writers: Price resistance → demand for free tier
- Content agencies: Cautious adoption → ROI comparison with alternatives
- Enterprise: Compliance concerns → security audit requests
- Risk signal: Freelancer backlash on social media within first 48 hours
```

### Example 2: Policy Change
```
Input: "Our platform is changing from free to freemium. 
How will our 50K existing users respond?"

Output:
- Power users: Mixed — some upgrade, some migrate
- Casual users: Majority churn unless retention hooks are strong
- Risk signal: Vocal minority organizing migration campaigns
- Recommendation: Phased rollout with grandfather clause for early adopters
```

## Validation

- [ ] Simulation completes with 3+ interacting personas
- [ ] Result card includes trajectory, risk signals, and follow-up questions
- [ ] Counterfactual queries produce meaningfully different outcomes
- [ ] Knowledge graph correctly maps actors and relationships

## Common Pitfalls

| Pitfall | Solution |
|---------|----------|
| Too few personas | Include at least 3 stakeholder types with conflicting interests |
| Vague scenario | Add specific constraints: timeline, budget, existing commitments |
| Ignoring counterfactuals | Always test at least one alternative variable |

## References

- [Live Application](https://mirofish.homes/)
- [GitHub Repository](https://github.com/666ghj/MiroFish)
- [Design Case Study](https://mirofish.homes/)