Skill UI

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

This skill simulates a four-voice council (Architect, Skeptic, Pragmatist, Critic) to resolve complex decisions marked by ambiguity or multiple trade-offs. It is designed for high-level strategic choices, identifying blind spots, and determining go/no-go decisions when a clear winner is absent. It specializes in synthesizing diverse perspectives to mitigate risk.

Spec-Driven Development (SDD) is a structured methodology that mandates defining comprehensive specifications before writing any code. It guides users through a gated process: SPECIFY -> PLAN -> TASKS -> IMPLEMENT. This workflow ensures all assumptions are surfaced, requirements are concrete, and the final product has clear success criteria, minimizing ambiguity and architectural risk in large projects.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.

This comprehensive, guided 4-phase workflow helps teams establish clear identity, boundaries, and strategic positioning. It guides users through absorbing existing context, auditing current capabilities versus claimed responsibilities, articulating a defined mission, and finally achieving stakeholder alignment. Use this framework when starting a new role, inheriting ambiguity, or needing to define clear operational boundaries.

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

This skill transforms vague, unclear, or half-formed business opportunities and brief descriptions into highly structured, actionable problem statements. It guides the user through a three-stage process: reframing the core questions, rigorously defining in-scope and out-of-scope boundaries, and designing a minimum viable research plan. It ensures that ambiguous inputs are converted into clear, executable steps, allowing the user to respond with confidence rather than asking for more clarification meetings.

This skill converts vague business questions or monitoring needs into a complete, structured, and implementation-ready dashboard specification. It guides users to define key metrics (KPIs), chart types, axes, filters, and optimal layout, providing data engineers and BI developers with all necessary details to build the dashboard without ambiguity.

This skill converts ambiguous or general research needs into a single, highly precise, self-contained prompt. It guides the user to define the project context, the core question, and specific sub-questions, ensuring the resulting prompt is rigorous enough for deep, multi-source AI research, minimizing ambiguity and guesswork.

This skill simulates a four-voice council (Architect, Skeptic, Pragmatist, Critic) to resolve complex decisions marked by ambiguity or multiple trade-offs. It is designed for high-level strategic choices, identifying blind spots, and determining go/no-go decisions when a clear winner is absent. It specializes in synthesizing diverse perspectives to mitigate risk.

A comprehensive guide for academic authors on how to strategically position their research contribution within specialized fields like risk, uncertainty, and decision theory. It details how to engage with canonical academic debates (e.g., Expected Utility critiques, Prospect Theory, Ambiguity) by citing primary literature and clearly defining the novelty relative to the closest prior work, enhancing the paper's academic rigor.

This guide helps researchers determine if their core contribution is focused on measuring or refining a specific risk or uncertainty primitive (e.g., ambiguity attitudes, VSL, probability weighting). It provides a framework to assess scope fit and distinguish between behavioral anomalies, methodological findings, and foundational risk theory, ensuring the paper is positioned correctly for high-tier academic journals like JRU.