Audit AI agent workspaces for authority, secrets, data flow, memory, supply-chain, and evidence risks—locally and without executing target code.
Turn your AI pilot data into a traceable decision pack with TCO, ROI, NPV, payback, sensitivity analysis, evidence gaps, risks, and clear next steps.
Three independent AI executives — Strategy, Tech, and Growth — blind-review your business decision and cross-examine each other before delivering one verdict.
Check whether your AI initiative has enough traceable evidence for a credible value review, and receive a readiness score, evidence gaps, and clear next steps.
Four independent AI reviewers — Security Skeptic, Reliability Realist, Maintainability Pragmatist, and Performance Pessimist — each review your diff blind to the others' verdicts, so real bugs don't get averaged away into one polite summary.
Production-grade code quality standards for AI coding agents: no-hacks policy, core values hierarchy, universal clean code principles, language-agnostic pitfalls, multi-stack security checks, and TypeScript-first depth with coverage-gap patterns. Grounded in real production failure modes, with standards and tooling spanning 19 languages.
Expert in end-to-end AI-assisted software delivery, from design engineering and design systems to shipping: design engineering, design system automation, technical documentation, spec-driven planning, polyglot code quality, systematic QA, release auditing, LLM context engineering, and Astro frontend optimization. Built for agent-agnostic workflows across the full 9-skill delivery lifecycle.
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.