Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add swarm-ai-research/swarm --skill run-scenariogit clone --depth 1 https://github.com/swarm-ai-research/swarmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/swarm-ai-research/swarm/run-scenario)<a href="https://agentmods.dev/skills/swarm-ai-research/swarm/run-scenario"><img src="https://agentmods.dev/badge/skills/swarm-ai-research/swarm/run-scenario/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/swarm-ai-research/swarm/run-scenario"><img src="https://agentmods.dev/badge/skills/swarm-ai-research/swarm/run-scenario.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.00739 |
| Opus 5.5 | $0.00006 | $0.00296 |
| Sonnet 5.5 | $0.00003 | $0.00148 |
| Haiku 4.5 | $0.00001 | $0.00074 |
Grade A, and why
run-scenario scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Scenario Skill
Execute a single SWARM scenario with a given seed and export all artifacts to a standardized output directory.
Prerequisites
swarm-safetypackage installed (pip install swarm-safetyorpip install -e /root/swarm-package/)- Scenario YAML file available (typically in
/root/scenarios/)
Procedure
1. Resolve the scenario path
Scenario references can be shorthand or full paths:
baseline→scenarios/baseline.yamlscenarios/baseline.yaml→ use as-is/root/scenarios/baseline.yaml→ use as-is
import 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}")
2. Run the simulation
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()
3. Export artifacts
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)
4. Extract key metrics
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}")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 119 lines · 14 tokens per session scan A 8388a78f8f90
run-scenario is a skill published in the GitHub repository swarm-ai-research/swarm (48 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 739 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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