Skill UI

A rigorous method for authorized adversarial security review. It models the attacker, maps the entire attack surface (APIs, auth flows, data handling), and generates reports containing only findings with a concrete, reproducible exploit path and verified mitigation steps. Essential for pre-launch hardening and critical system reviews.

A rigorous methodology for debugging complex anomalies or incidents when multiple plausible causes exist. It guides the user to enumerate competing, falsifiable hypotheses and prioritizes running the cheapest, most discriminating observation first. By iteratively eliminating hypotheses and preferring the least-assumptive survivor, it systematically localizes the root cause efficiently.

A rigorous framework for tracing the full systemic consequence chain of strategic decisions. It guides users beyond the immediate, intended first-order effects to anticipate delayed impacts, incentive shifts, and complex problems that only emerge at scale or over time, ensuring decisions are robust against unforeseen systemic risks.

This technique forces rigorous critical evaluation by simulating the construction of the strongest, most faithful opposing argument (the 'steel man'). Instead of accepting weak counterarguments or falling into confirmation bias, the process generates a robust adversarial case. It is essential for high-stakes decision reviews, architectural debates, and major proposal vetting, ensuring that the final decision is based on a comprehensive understanding of all risks and conditions for change.

This framework provides a rigorous pre-execution review mechanism for proposed actions by AI agents or systems. It functions as a critical safety boundary, auditing the entire decision path—from initial request to proposed tool execution—without authorizing or executing anything. It verifies action conformity, evidence integrity, temporal validity, and reviewer independence to ensure system compliance and security.