Skill UI

This methodology provides a comprehensive framework for estimating buffered numbers—such as capacity limits, timeframes, or budgets—when making commitments under uncertainty. It guides users to calculate a buffer based on both residual estimation error and the potential cost of failure (breach cost), ensuring resilience against costly underestimation in critical systems or plans.

This tool acts as a meta-skill guide, helping users systematically determine the most appropriate cognitive framework when faced with complex problem-solving tasks. It maps the problem's domain and type (e.g., diagnosis, strategy, architecture) to select the optimal primary thinking skill, preventing guesswork or arbitrary stacking of tools. It ensures the chosen method is justified by mechanism fit and relevance.

A rapid decision-making framework (Observe, Orient, Decide, Act) designed for high-pressure, dynamic environments. It advocates acting on high-probability signals (e.g., 70% confidence) immediately and cycling through observation and action quickly, rather than waiting for perfect certainty. Ideal for incident response, debugging moving targets, and time-bound crisis management.

A rigorous decision-making methodology that quantifies the trade-offs when allocating scarce resources (time, budget, attention). Instead of merely evaluating a chosen path, this framework forces the user to identify the single best alternative forgone, calculating the net value delta (chosen value minus best forgone value). It is essential for feature prioritization, major commitments, and strategic planning to prevent decisions based solely on immediate perceived gains.

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.

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.

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.

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 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.