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run-scenario
Execute a SWARM simulation scenario and export standardized artifacts
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
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Execute a SWARM simulation scenario and export standardized artifacts
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
SWARM: System-Wide Assessment of Risk in Multi-agent systems. Simulate multi-agent dynamics, test governance, study emergent risks.
Query the SWARM knowledge graph structurally — find pages, list backlinks, follow link paths, and surface related/semantic neighbors across docs, scenarios, slash commands, agents, roles, and code references. Prefer this over grep when you need *connected* answers ("what links to X", "how does X relate to Y", "what's similar to X").
Scaffold research papers from SWARM run data with auto-populated tables
Run parameter grid sweeps across SWARM scenarios and generate summary statistics
Generate publication-quality visualizations from SWARM simulation data
Perform rigorous statistical analysis on SWARM experiment data with multiple-comparison corrections
| name | run-scenario |
| description | Execute a SWARM simulation scenario and export standardized artifacts |
| version | 1.0 |
| domain | swarm-safety |
| triggers | ["run scenario","execute simulation","baseline run"] |
Execute a single SWARM scenario with a given seed and export all artifacts to a standardized output directory.
swarm-safety package installed (pip install swarm-safety or pip install -e /root/swarm-package/)/root/scenarios/)Scenario references can be shorthand or full paths:
baseline → scenarios/baseline.yamlscenarios/baseline.yaml → use as-is/root/scenarios/baseline.yaml → use as-isimport os
def resolve_scenario(ref: str) -> str:
"""Resolve a scenario reference to a full path."""
candidates = [
ref,
f"scenarios/{ref}.yaml",
f"/root/scenarios/{ref}.yaml",
f"scenarios/{ref}",
]
for c in candidates:
if os.path.isfile(c):
return c
raise FileNotFoundError(f"Cannot find scenario: {ref}")
Use the SWARM CLI to execute:
python -m swarm run <scenario_path> --seed <seed> --epochs <N> --steps <M>
Or programmatically:
from swarm.core.orchestrator import Orchestrator
from swarm.scenarios.loader import load_scenario
config = load_scenario(scenario_path)
# Override simulation parameters if needed
config["simulation"]["seed"] = seed
config["simulation"]["n_epochs"] = epochs
config["simulation"]["steps_per_epoch"] = steps
orch = Orchestrator(config)
result = orch.run()
After the run completes, export to the output directory:
import json
import os
os.makedirs(output_dir, exist_ok=True)
# Export history.json
with open(os.path.join(output_dir, "history.json"), "w") as f:
json.dump(result.to_dict(), f, indent=2)
# Export CSV metrics
csv_dir = os.path.join(output_dir, "csv")
os.makedirs(csv_dir, exist_ok=True)
result.export_csv(csv_dir)
The final epoch snapshot contains summary metrics:
history = result.to_dict()
final = history["epoch_snapshots"][-1]
welfare = final["welfare"]
toxicity = final["toxicity_rate"]
print(f"Final welfare: {welfare:.3f}")
print(f"Final toxicity: {toxicity:.3f}")
<output_dir>/
├── history.json # Full simulation history
└── csv/
├── epoch_metrics.csv # Per-epoch aggregate metrics
└── agent_metrics.csv # Per-agent per-epoch metrics
swarm-safety is installed with runtime deps: pip install swarm-safety[runtime]/root/scenarios/ for available YAMLs