MiroFish-Offline Skill

Skill by ara.so — Daily 2026 Skills collection.

MiroFish-Offline is a fully local multi-agent swarm intelligence engine. Feed it any document (press release, policy draft, financial report) and it generates hundreds of AI agents with unique personalities that simulate public reaction on social media — posts, arguments, opinion shifts — hour by hour. No cloud APIs required: Neo4j CE 5.15 handles graph memory, Ollama serves the LLMs.

Architecture Overview

Document Input
     │
     ▼
Graph Build (NER + relationship extraction via Ollama LLM)
     │
     ▼
Neo4j Knowledge Graph (entities, relations, embeddings via nomic-embed-text)
     │
     ▼
Env Setup (generate hundreds of agent personas with personalities + memory)
     │
     ▼
Simulation (agents post, reply, argue, shift opinions on simulated platforms)
     │
     ▼
Report (ReportAgent interviews focus group, queries graph, generates analysis)
     │
     ▼
Interaction (chat with any individual agent, full memory persists)

Backend: Flask + Python 3.11
Frontend: Vue 3 + Node 18
Graph DB: Neo4j CE 5.15 (bolt protocol)
LLM: Ollama (OpenAI-compatible /v1 endpoint)
Embeddings: nomic-embed-text (768-dimensional, via Ollama)
Search: Hybrid — 0.7 × vector similarity + 0.3 × BM25

Installation

Option A: Docker (Recommended)

git clone https://github.com/nikmcfly/MiroFish-Offline.git
cd MiroFish-Offline
cp .env.example .env

# Start Neo4j + Ollama + MiroFish backend + frontend
docker compose up -d

# Pull required models into the Ollama container
docker exec mirofish-ollama ollama pull qwen2.5:32b
docker exec mirofish-ollama ollama pull nomic-embed-text

# Check all services are healthy
docker compose ps

Open http://localhost:3000.

Option B: Manual Setup

1. Neo4j

docker run -d --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/mirofish \
  neo4j:5.15-community

2. Ollama

ollama serve &
ollama pull qwen2.5:32b       # Main LLM (~20GB, requires 24GB VRAM)
ollama pull qwen2.5:14b       # Lighter option (~10GB VRAM)
ollama pull nomic-embed-text  # Embeddings (small, fast)

3. Backend

cp .env.example .env
# Edit .env (see Configuration section)

cd backend
pip install -r requirements.txt
python run.py
# Backend starts on http://localhost:5000

4. Frontend

cd frontend
npm install
npm run dev
# Frontend starts on http://localhost:3000

Configuration (.env)

# ── LLM (Ollama OpenAI-compatible endpoint) ──────────────────────────
LLM_API_KEY=ollama
LLM_BASE_URL=http://localhost:11434/v1
LLM_MODEL_NAME=qwen2.5:32b

# ── Neo4j ─────────────────────────────────────────────────────────────
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=mirofish

# ── Embeddings (Ollama) ───────────────────────────────────────────────
EMBEDDING_MODEL=nomic-embed-text
EMBEDDING_BASE_URL=http://localhost:11434

# ── Optional: swap Ollama for any OpenAI-compatible provider ─────────
# LLM_API_KEY=$OPENAI_API_KEY
# LLM_BASE_URL=https://api.openai.com/v1
# LLM_MODEL_NAME=gpt-4o

Core Python API

GraphStorage Interface

The abstraction layer between MiroFish and the graph database:

from backend.storage.base import GraphStorage
from backend.storage.neo4j_storage import Neo4jStorage

# Initialize storage (typically done via Flask app.extensions)
storage = Neo4jStorage(
    uri=os.environ["NEO4J_URI"],
    user=os.environ["NEO4J_USER"],
    password=os.environ["NEO4J_PASSWORD"],
    embedding_model=os.environ["EMBEDDING_MODEL"],
    embedding_base_url=os.environ["EMBEDDING_BASE_URL"],
    llm_base_url=os.environ["LLM_BASE_URL"],
    llm_api_key=os.environ["LLM_API_KEY"],
    llm_model=os.environ["LLM_MODEL_NAME"],
)

Building a Knowledge Graph from a Document

from backend.services.graph_builder import GraphBuilder

builder = GraphBuilder(storage=storage)

# Feed a document string
with open("press_release.txt", "r") as f:
    document_text = f.read()

# Extract entities + relationships, store in Neo4j
graph_id = builder.build(
    content=document_text,
    title="Q4 Earnings Report",
    source_type="financial_report",
)

print(f"Graph built: {graph_id}")
# Returns a graph_id used for subsequent simulation runs

Creating and Running a Simulation

from backend.services.simulation import SimulationService

sim = SimulationService(storage=storage)

# Create a simulation environment from an existing graph
sim_id = sim.create_environment(
    graph_id=graph_id,
    agent_count=200,           # Number of agents to generate
    simulation_hours=24,       # Simulated time span
    platform="twitter",        # "twitter" | "reddit" | "weibo"
)

# Run the simulation (blocking — use async wrapper for production)
result = sim.run(sim_id=sim_id)

print(f"Simulation complete. Posts generated: {result['post_count']}")
print(f"Sentiment trajectory: {result['sentiment_over_time']}")

Querying Simulation Results

from backend.services.report import ReportAgent

report_agent = ReportAgent(storage=storage)

# Generate a structured analysis report
report = report_agent.generate(
    sim_id=sim_id,
    focus_group_size=10,    # Number of agents to interview
    include_graph_search=True,
)

print(report["summary"])
print(report["key_narratives"])
print(report["sentiment_shift"])
print(report["influential_agents"])

Chatting with a Simulated Agent

from backend.services.agent_chat import AgentChatService

chat = AgentChatService(storage=storage)

# List agents from a completed simulation
agents = chat.list_agents(sim_id=sim_id, limit=10)
agent_id = agents[0]["id"]

print(f"Chatting with: {agents[0]['persona']['name']}")
print(f"Personality: {agents[0]['persona']['traits'
how to use mirofish-offline-simulation
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use mirofish-offline-simulation on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add mirofish-offline-simulation
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/aradotso/trending-skills --skill mirofish-offline-simulationcopy
The skills CLI fetches mirofish-offline-simulation from GitHub repository aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/mirofish-offline-simulation
Reload or restart Cursor to activate mirofish-offline-simulation. Access the skill through slash commands (e.g., /mirofish-offline-simulation) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?