openai-jupyter-notebook

Configures mewt or muton mutation testing campaigns — scopes targets, tunes timeouts, and optimizes long-running runs. Use when the user mentions mewt, muton, mutation testing, or wants to configure or optimize a mutation testing campaign.

Builds multi-language source code graphs for security analysis: call graphs, attack surface mapping, blast radius, taint propagation, complexity hotspots, and entry point enumeration. Generates Mermaid diagrams (call graphs, class hierarchies, dependency maps, heatmaps). Compares code graph snapshots for structural diff and evolution analysis. Runs graph-informed mutation testing triage (genotoxic). Generates mutation-driven test vectors (vector-forge). Extracts crypto protocol message flows and converts Mermaid diagrams to ProVerif models. Projects SARIF and weAudit findings onto code graphs. Use when analyzing call paths, mapping attack surface, visualizing code architecture, triaging survived mutants, generating cryptographic test vectors, diagramming crypto protocols, formally verifying protocols, or augmenting audits with static analysis findings.

Annotates codebases with dimensional analysis comments documenting units, dimensions, and decimal scaling. Use when someone asks to annotate units in a codebase, perform a dimensional analysis, or find vulnerabilities in a DeFi protocol. Prevents dimensional mismatches and catches formula bugs early.

Detects missing or compiler-optimized zeroization of sensitive data with assembly and control-flow analysis

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations (Claude Code Action, Gemini CLI, OpenAI Codex, GitHub AI Inference)