Skill UI

This guide helps researchers structure and refine academic papers for the Journal on Communications (JOC). It provides a framework for separating core, decision-making content (main text) from supporting details (appendices/supplementary material), ensuring the manuscript remains scientifically rigorous yet adheres to strict length constraints (e.g., ~12000 characters). It teaches how to use a 'decisive three-question' standard.

This skill provides a comprehensive guide for evaluating whether a research manuscript aligns with the scope, structure, and requirements of the journal *Soft Science*. It covers journal positioning, topic preferences, recommended writing structure, common rejection pitfalls, and strategic revision advice for academic papers focused on policy, regional development, and management decision-making.

A comprehensive tool designed to help developers compare multiple technical approaches for solving a single problem. It systematically identifies and analyzes the trade-offs, complexity, performance, and learning curves associated with different design patterns (e.g., state management, data fetching, API design). Ideal for architectural reviews and making informed, robust technology stack decisions.

This protocol simulates a diverse, anonymous jury of 3 to 5 specialized subagents to reach a committed, non-ambiguous verdict. It is designed to combat cognitive biases like anchoring and groupthink by running blind, independent opinions before structured deliberation. Ideal for high-stakes decisions across product development, architecture, strategy, and vendor choice where a single perspective is insufficient.

This skill guides decision-making when resources (time, context, tools) are finite and the search space is infinite. It instructs the user to first establish a clear, explicit 'good enough' aspiration level. Once set, the process involves stopping at the first option that meets this threshold, preventing costly over-optimization and wasted effort.

A strategic framework for making decisions when facing extreme uncertainty and unclear outcomes. Effectuation instructs users to start with available means (skills, assets, network) and define an affordable loss limit. Progress is driven by taking small, controlled actions to gain real commitments and information, allowing the ultimate goal to emerge naturally.

A rigorous methodology for systematic problem diagnosis and consequential decision-making. It operates in two modes: Problem Analysis (for root cause identification using IS/IS-NOT contrast) and Decision Analysis (for prioritizing options by screening MUSTs, weighting WANTs, and assessing adverse consequences). Ideal for complex engineering and business scenarios requiring structured evidence and risk assessment.

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.

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.

Build a production-ready behavior tree runtime with Blackboard, action/condition leaves, and composites. Implement Utility AI using response curves and action evaluators. Create hybrid agents combining both for complex NPC decision-making beyond simple FSMs. Suitable for game development and AI system architecture.