swarm-2SWARM: System-Wide Assessment of Risk in Multi-agent systems. 38 agent types, 29 governance levers, 55 scenarios. Study emergent risks, phase transitions, and governance cost paradoxes.
Install via ClawdBot CLI:
clawdbot install rsavitt/swarm-2Grade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
POST → http://localhost:8000/api/v1/agents/registerCalls external URL not in known-safe list
https://github.com/swarm-ai-safety/swarmAI Analysis
The skill runs simulations locally with API bound to localhost and CORS restricted, preventing external data exfiltration. The external GitHub URL is for documentation/cloning, not data transmission. No credential harvesting or obfuscation is present.
Audited Apr 17, 2026 · audit v1.0
Generated Mar 22, 2026
Simulate a multi-agent trading environment with honest, opportunistic, and adversarial agents to study emergent collusion risks and market manipulation. Use SWARM's soft probabilistic labels to quantify toxicity rates and quality gaps, helping financial institutions assess governance levers for automated trading systems.
Model interactions between AI agents representing healthcare providers, insurers, and patients to detect deceptive or adversarial behaviors that could compromise patient welfare. Analyze incoherence and illusion delta metrics to identify systemic failures in care coordination and governance cost paradoxes.
Deploy SWARM with agents mimicking suppliers, logistics providers, and manufacturers to study phase transitions and emergent risks under disruptions like shortages or fraud. Evaluate conditional loss and toxicity rates to optimize governance strategies for supply chain integrity.
Use SWARM's 55 scenarios to simulate user-agent interactions, including honest and deceptive types, to assess content moderation systems for toxicity and adverse selection. Apply probabilistic labels to measure quality gaps and inform platform governance policies against misinformation.
Simulate a swarm of AI-controlled vehicles with different agent behaviors to study emergent collision risks and coordination failures. Leverage SWARM's 38 agent types to analyze incoherence and illusion delta, aiding in the design of safety protocols for autonomous transportation networks.
Offer SWARM as a cloud-based service where companies pay subscription fees to run custom multi-agent simulations for safety testing. Provide analytics dashboards for metrics like toxicity rate and quality gap, with tiered pricing based on scenario complexity and agent counts.
Provide expert services to integrate SWARM into clients' existing AI systems for governance and risk assessment. Develop tailored scenarios and agent types, with revenue from project-based fees and ongoing support contracts for simulation analysis and optimization.
License SWARM's framework to academic institutions, government agencies, and large corporations for internal research on AI safety. Generate revenue through licensing agreements, training workshops, and partnerships for developing new agent types or governance levers.
💬 Integration Tip
Install via pip and start with the CLI to list scenarios; for API use, ensure localhost binding and add authentication in production to secure simulations.
Scored Apr 19, 2026
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