Skill UI

A rigorous framework for making evidence-based estimates and forecasts when precise data is unavailable. It guides users to anchor predictions on base rates, articulate uncertainty using confidence intervals, and systematically update beliefs using likelihood ratios (prior to posterior) as new evidence arrives. Ideal for risk sizing, project timelines, and strategic decision-making.

This framework guides decision-makers to structure their thinking by classifying decisions based on the cost and feasibility of reversal. It emphasizes treating low-cost, reversible choices (two-way doors) quickly, while reserving deep deliberation for high-cost, irreversible commitments (one-way doors). It helps mitigate analysis paralysis and premature over-engineering.

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 methodology guides users to ask critical, load-bearing questions when faced with ambiguous, vague, or assumption-laden requirements. It helps surface hidden requirements and undefined terms, ensuring that the development process is based on explicit, agreed-upon facts rather than guesswork. Use it to define the true problem and scope before committing to a solution.

A comprehensive methodology for diagnosing complex systems where problems are emergent across multiple components. It guides the user to map system boundaries, stocks and flows, feedback loops, and underlying structural drivers. By identifying the highest feasible leverage point, it recommends structural interventions that address root causes rather than superficial symptoms.

This framework, known as Via Negativa, guides iterative improvement by prioritizing removal and simplification over adding new features. It mandates that before introducing any new complexity, dependency, or layer, one must exhaust all possibilities of eliminating redundant, harmful, or nonessential elements. It emphasizes evidence-based decision-making and risk assessment, ensuring that new additions are truly necessary after thorough subtraction passes.

This agent is designed to serve as a comprehensive and helpful assistant, capable of guiding users through a wide variety of tasks and inquiries. It acts as a general knowledge resource and helper, providing information, suggestions, and support across multiple domains whenever the user needs assistance.

This skill acts as the central orchestrator for multi-stage, complex penetration testing engagements. It guides the entire process—from initial reconnaissance (asset mapping, data leakage hunting) through boundary penetration (web exploits, supply chain attacks) to post-exploitation planning. It is essential for planning end-to-end attack paths, coordinating specialized sub-skills, and ensuring structured reporting of findings.