Mirofish

MiroFish Skill

MiroFish is a multi-agent swarm intelligence simulation engine.

• Base URL: http://10.0.0.100:5001
• Auth: None required
• CLI: bash /root/.openclaw/workspace/skills/mirofish/scripts/mirofish.sh <command>

Simulations can take several minutes to complete. Always poll status after triggering long-running operations (build, prepare, run, report).

CLI Reference

SCRIPT="bash /root/.openclaw/workspace/skills/mirofish/scripts/mirofish.sh"

Health

$SCRIPT health
# → {"status":"ok","service":"MiroFish Backend"}

Projects

# List all projects
$SCRIPT projects

# List with limit
$SCRIPT projects --limit 5

# Get project details
$SCRIPT project <project_id>

# Delete a project
$SCRIPT project-delete <project_id>

Upload & Ontology Generation

Upload one or more seed files and generate an ontology. This creates a project.

$SCRIPT upload /path/to/seed.pdf \
  --name "Market Analysis 2025" \
  --requirement "Simulate how retail investors react to Fed rate decisions" \
  --context "Focus on Reddit and Twitter sentiment"

Returns a project_id and a background task_id. Poll with task <task_id>.

Graph Build

Build a knowledge graph from a project. Returns a task_id.

$SCRIPT build <project_id>
# optionally specify a graph name:
$SCRIPT build <project_id> "my-graph-name"

Tasks

# Check a single task
$SCRIPT task <task_id>

# List all tasks
$SCRIPT tasks

Task statuses: pending, running, completed, failed

Nodes

# Current default graph nodes
$SCRIPT graph-nodes

# Specific graph
$SCRIPT graph-nodes <graph_id>

Entities

# All entities in a graph
$SCRIPT entities <graph_id>

# Single entity detail
$SCRIPT entity <graph_id> <entity_uuid>

Filter by type via curl:

curl -s "http://10.0.0.100:5001/api/simulation/entities/<graph_id>/by-type/<type>"

Simulation Management

# List all simulations
$SCRIPT sim-list

# Get simulation details
$SCRIPT sim-get <simulation_id>

# Create simulation (auto-detects graph_id from project; or pass explicitly)
$SCRIPT sim-create <project_id>
$SCRIPT sim-create <project_id> <graph_id>

# Prepare simulation environment (takes several minutes)
$SCRIPT sim-prepare <simulation_id>

# Check preparation / run status
$SCRIPT sim-status <simulation_id>

# Start the simulation run
$SCRIPT sim-run <simulation_id>

# Stop a running simulation
$SCRIPT sim-stop <simulation_id>

Interview an Agent

After a simulation has run, you can interview individual agents:

$SCRIPT interview <simulation_id> <agent_id> "What do you think about the Fed's decision?"

Reports

# List all reports
$SCRIPT report-list

# Get a specific report
$SCRIPT report-get <simulation_id>

# Generate a prediction report (async, poll status after)
$SCRIPT report <simulation_id>

# Check report generation status
$SCRIPT report-status <simulation_id>

# Chat with the ReportAgent
$SCRIPT report-chat <simulation_id> "Summarize the key findings"

Full Simulation Workflow

This is the complete pipeline from raw data to actionable predictions.

Step 1 — Upload seed file & generate ontology

$SCRIPT upload research_paper.pdf \
  --name "Fed Policy Sim" \
  --requirement "How will retail investors react to a 50bp rate hike?" \
  --context "Social media focus: Reddit r/investing, Twitter finance accounts"
# → returns project_id + task_id

Step 2 — Wait for ontology task to complete

$SCRIPT task <task_id>
# poll until status == "completed"

Step 3 — Build knowledge graph

$SCRIPT build <project_id>
# → returns task_id
$SCRIPT task <task_id>
# poll until completed → note graph_id from result

Step 4 — Create simulation

$SCRIPT sim-create <project_id> <graph_id>
# → returns simulation_id

Step 5 — Prepare simulation (agent profiling, ~5–15 min)

$SCRIPT sim-prepare <simulation_id>
# → returns task_id
$SCRIPT sim-status <simulation_id>
# poll until preparation complete

Step 6 — Run simulation

$SCRIPT sim-run <simulation_id>
$SCRIPT sim-status <simulation_id>
# poll until run complete

Step 7 — Generate prediction report

$SCRIPT report <simulation_id>
$SCRIPT report-status <simulation_id>
# poll until report generated

Step 8 — Interact with results

# Chat with the ReportAgent for analysis
$SCRIPT report-chat <simulation_id> "What are the top 3 predicted outcomes?"

# Interview a specific agent
$SCRIPT entities <graph_id>   # find agent UUIDs
$SCRIPT interview <simulation_id> <agent_uuid> "How did you weigh the rate hike impact?"

Agent Mode

When called from a non-TTY context (agent/pipeline mode), all commands output JSON envelopes:

{
  "ok": true,
  "command": "sim-status",
  "result": { ... },
  "next_actions": []
}

Environment

DEPRECATED

This skill is deprecated. MiroFish has been replaced by MiroShark.

See: https://github.com/phattbeats/MiroShark Archived skill: /root/.openclaw/workspace/skills/mirofish-deprecated/