babywyrm

Run Agent Smith in CTF mode for Capture The Flag challenges. Use when doing CTF, finding flags, hunting exploitable vulnerabilities, or analyzing challenge code for quick wins.

Add new static analysis rules to Agent Smith's rule engine. Use when creating detection rules, adding regex patterns for vulnerabilities, or extending the static scanner.

Interpret and triage Agent Smith scan findings. Use when reading scan results, understanding severity ratings, or deciding what to fix first.

Start, configure, and use the Agent Smith MCP server from Cursor. Use when setting up the MCP server, running scans via MCP tools, or integrating Agent Smith into Cursor IDE workflows.

Choose and combine AI analysis profiles for Agent Smith scans. Use when selecting profiles, combining multiple profiles, or understanding which profile fits a target codebase.

Run Agent Smith security scans with the right mode, preset, and flags. Use when scanning code for vulnerabilities, running security audits, or when the user mentions agentsmith, security scan, or vulnerability analysis.

Create a new mcpnuke security check module end-to-end: pattern file, check function, wire into run_all_checks, add tests, update changelog. Use when adding a new security check to mcpnuke.

Add new regex detection patterns to mcpnuke's pattern libraries with test coverage. Use when adding new patterns to rules.py or probes.py, or when extending detection for a vulnerability class.

Run mcpnuke's test suite, interpret failures, and fix issues. Use when running tests, debugging test failures, or verifying changes in mcpnuke.

Add a new transport type to mcpnuke alongside SSE, HTTP, and ToolServer. Use when adding support for a new MCP transport protocol like DPoP, mTLS, or a custom gateway.

Add new DVMCP challenge test classes to mcpnuke's test suite following the established pattern. Use when adding tests for a new vulnerability type or DVMCP challenge.

Use Stoneburner's inference.env standard and brain utilities to route model evaluation through a selected backend. Use when reading or writing INFERENCE_BACKEND, INFERENCE_URL, INFERENCE_MODEL, INFERENCE_THINK, /opt/agentic/inference.env, atomics.inference, provider_from_target, brain-status, brain-switch, or brain-vllm.

Evaluate models for AI-backed application, workflow, or challenge compatibility with Stoneburner. Use when running atomics qa, adversarial, sweep, provider-test, model promotion, CTF solvability checks, guardrail regression, or classifying models as function-compatible, walkthrough-compatible, too safe, unsafe, or broken.

Create and review Stoneburner target profiles for app-level AI gates and custom inference endpoints. Use when working with profiles/examples, profiles/local, atomics qa --profile, soak --profile, stress --profile, scenario profile workloads, HTTP target profiles, Ollama target profiles, response parsing, or classification rules.

Drive representative agentic flows through the Zero-Trust Agentic Control Plane prototype and visualize what each layer (OPA waypoint, nullfield, Envoy AI Gateway, Gatekeeper, NetworkPolicy) delivers. Use when someone wants to demo, inspect, or verify the zero-trust blueprint — "show me the flows", "what does each layer do", "watch the gating", "is the control plane working" — against a running cluster (k3s/EKS/kind) that has the blueprint deployed.