Review AI agents, automation workflows, and project architectures for design gaps, risks, missing requirements, and improvement opportunities.
Audit AI agent workspaces for authority, secrets, data flow, memory, supply-chain, and evidence risks—locally and without executing target code.
Three reviewer lenses audit an AI agent's or contractor's done report, cross-examine each other, and give an accept/do-not-accept recommendation.
Turn your AI pilot data into a traceable decision pack with TCO, ROI, NPV, payback, sensitivity analysis, evidence gaps, risks, and clear next steps.
An interactive Claude Skill that generates a customized, security-reviewed Terraform repository for Amazon Bedrock AgentCore, IAM least-privilege policies, Knowledge Base + vector store, Gateway tool registration, and Runtime deployment, scoped to your actual agent instead of a generic template dump.
Audit AI agents, Skills, MCP configurations, tool permissions, and coding-agent setups for prompt injection, excessive agency, credential exposure, data leakage, unsafe code execution, supply-chain risks, memory risks, missing approval controls, and runaway autonomy. Get a calibrated Security Score, Audit Confidence, evidence-backed findings, remediation steps, verification checks, and residual-risk analysis.
Audit persistent AI agents for memory contamination, context drift, stale authorization, identity conflicts, and unsafe tool authority. Get evidence-backed findings, minimal corrections, and regression tests.
This Skill is built on practical expertise in AI agent safety, prompt-injection defense, source-to-sink privacy review, tool and MCP metadata integrity, memory provenance checks, autonomous action scoping, and evidence-based verification. It helps agents separate instructions from evidence before tool calls, uploads, publishing, deletion, code execution, or network output.