How do I install the Mirofish Prediction skill?

Run npx skillmds@latest add kmshihab7878/mirofish-prediction in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Mirofish Prediction safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Mirofish Prediction?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Mirofish Prediction free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under AGPL-3.

Who published Mirofish Prediction?

kmshihab7878 (@kmshihab7878) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mirofish Prediction by kmshihab7878

Name: Mirofish Prediction
Author: kmshihab7878

Mirofish Prediction

Qualitative scenario prediction via MiroFish swarm simulation. Builds simulated digital worlds from seed data (news, market signals, policy changes), runs thousands of agents with behavioral logic, and produces narrative prediction reports. Use as complement to TimesFM quantitative forecasts for trading and strategy decisions.

kmshihab7878 Updated Sep 17, 2026

File contents

MiroFish Scenario Prediction

Overview

MiroFish is a swarm intelligence engine that builds simulated digital worlds from real-world seed data. Thousands of agents with distinct personalities, memory, and behavioral logic interact to produce qualitative scenario predictions.

Complementary to TimesFM: TimesFM gives quantitative price curves with confidence intervals. MiroFish gives qualitative scenario narratives — what happens if a specific event occurs and how it cascades through markets and public opinion.

When to Use

"What happens if the Fed raises rates?" — scenario simulation
"Impact of a whale dumping 10K BTC?" — cascading effect analysis
Public opinion modeling for token launches or policy changes
Complementing TimesFM forecasts with narrative context
Any council question in the trading or strategy domain
Strategic decisions that need "what if" scenario exploration

How to Start MiroFish

# Backend (Python 3.10, port 5001)
source ~/Projects/MiroFish/backend/.venv/bin/activate
cd ~/Projects/MiroFish && python backend/app/main.py

# Frontend (port 3002 — avoid conflict with Agent Orchestrator on 3000)
PORT=3002 npm --prefix ~/Projects/MiroFish/frontend start

Prerequisites: Real API keys in ~/Projects/MiroFish/.env:

LLM_API_KEY — OpenRouter or Claude API key
ZEP_API_KEY — Zep Cloud key for agent memory

Integration with Council

When /council is invoked for trading or strategy domains:

Phase 0: If MiroFish is running, seed a scenario simulation with the council question
Feed the simulation results as additional intelligence to the advisors
The Contrarian advisor should specifically challenge MiroFish's assumptions
The Executor should reference both TimesFM numbers and MiroFish scenarios

Integration with /plan

When /plan routes a trading or strategy task:

Use MiroFish for "what-if" analysis before committing to a trading strategy
Compare MiroFish qualitative predictions against TimesFM quantitative forecasts
If predictions diverge significantly, escalate to council decision

Cost Warning

Each simulation runs thousands of LLM-powered agents. A single scenario can consume significant API tokens. Use judiciously — prefer TimesFM for routine forecasting, reserve MiroFish for high-stakes scenario analysis.

Combo: Full Prediction Stack

Aster MCP (live market data)
    → TimesFM (quantitative: price curves + confidence intervals)
    → MiroFish (qualitative: scenario narratives + cascading effects)
    → Council (multi-advisor synthesis of both)
    → autoresearch (measure prediction accuracy over time)

---name: mirofish-predictiondescription: Qualitative scenario prediction via MiroFish swarm simulation. Builds simulated digital worlds from seed data (news, market signals, policy changes), runs thousands of agents with behavioral logic, and produces narrative prediction reports. Use as complement to TimesFM quantitative forecasts for trading and strategy decisions.license: AGPL-3.0---# MiroFish Scenario Prediction## OverviewMiroFish is a swarm intelligence engine that builds simulated digital worlds fromreal-world seed data. Thousands of agents with distinct personalities, memory, andbehavioral logic interact to produce qualitative scenario predictions.**Complementary to TimesFM**: TimesFM gives quantitative price curves with confidenceintervals. MiroFish gives qualitative scenario narratives — what happens *if* a specificevent occurs and how it cascades through markets and public opinion.## When to Use- "What happens if the Fed raises rates?" — scenario simulation- "Impact of a whale dumping 10K BTC?" — cascading effect analysis- Public opinion modeling for token launches or policy changes- Complementing TimesFM forecasts with narrative context- Any council question in the trading or strategy domain- Strategic decisions that need "what if" scenario exploration## How to Start MiroFish```bash# Backend (Python 3.10, port 5001)source ~/Projects/MiroFish/backend/.venv/bin/activatecd ~/Projects/MiroFish && python backend/app/main.py# Frontend (port 3002 — avoid conflict with Agent Orchestrator on 3000)PORT=3002 npm --prefix ~/Projects/MiroFish/frontend start```**Prerequisites**: Real API keys in `~/Projects/MiroFish/.env`:- `LLM_API_KEY` — OpenRouter or Claude API key- `ZEP_API_KEY` — Zep Cloud key for agent memory## Integration with CouncilWhen `/council` is invoked for trading or strategy domains:1. **Phase 0**: If MiroFish is running, seed a scenario simulation with the council question2. Feed the simulation results as additional intelligence to the advisors3. The **Contrarian** advisor should specifically challenge MiroFish's assumptions4. The **Executor** should reference both TimesFM numbers and MiroFish scenarios## Integration with /planWhen `/plan` routes a trading or strategy task:1. Use MiroFish for "what-if" analysis before committing to a trading strategy2. Compare MiroFish qualitative predictions against TimesFM quantitative forecasts3. If predictions diverge significantly, escalate to council decision## Cost WarningEach simulation runs thousands of LLM-powered agents. A single scenario can consumesignificant API tokens. Use judiciously — prefer TimesFM for routine forecasting,reserve MiroFish for high-stakes scenario analysis.## Combo: Full Prediction Stack```Aster MCP (live market data) → TimesFM (quantitative: price curves + confidence intervals) → MiroFish (qualitative: scenario narratives + cascading effects) → Council (multi-advisor synthesis of both) → autoresearch (measure prediction accuracy over time)```

Frequently asked questions