Run controlled cybersecurity agent workflows with CAI
Use CAI to run research and bug-bounty-oriented security agents with model routing, tool integrations, tracing, MCP support, and human oversight.
npx skills add agentskillexchange/skills --skill run-controlled-cybersecurity-agent-workflows-with-cai
pip install cai-framework or create a virtual environment and run source cai_env/bin/activate && pip install cai-framework; configure model credentials in the environment before starting CAI.Use CAI when a security operator needs an agentic workflow for authorized vulnerability research, lab exercises, CTFs, bug bounty triage, or retesting. The operator installs the CAI framework, configures model access and environment variables, then runs specialized security agents with tracing, tool integrations, and optional MCP connections. Invoke this for controlled security testing and education; keep it scoped to authorized environments and reviewable security workflows rather than autonomous offensive activity.
What this skill actually does
Inputs and prerequisites: Python, cai-framework, model provider credentials, optional Docker and MCP servers.
Setup notes: Install with `pip install cai-framework` or create a virtual environment and run `source cai_env/bin/activate && pip install cai-framework`; configure model credentials in the environment before starting CAI.
Source and verification boundary: use https://aliasrobotics.github.io/cai/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Custom Agents workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.