Skill UI

This skill manages complex, stateful proof projects as a local-first pipeline. It guides the user through drafting, rigorous verification, and source bookkeeping. It systematically handles proof continuation across runs, maintaining a full audit trail. When local attempts stall, it orchestrates handoffs to external AI models (like GPT Pro) or performs optional deep audits (DeepSeek), ensuring a structured, traceable, and verifiable output suitable for academic or technical submission.

A comprehensive, multi-step framework for conducting systematic QA testing on web applications. This guide covers scope detection (diff-aware), tiered testing depth (Quick, Standard, Exhaustive), detailed issue taxonomy (Functional, Visual, UX, etc.), structured bug documentation, and the critical fix-and-verify loop, ensuring high code quality throughout the development cycle.

This protocol provides a systematic, multi-step diagnostic approach to determine if a command failure (e.g., permission denied, network issues) is genuinely due to sandbox restrictions or is caused by environmental or measurement errors. It includes eight critical checks—such as verifying shell scope and reading real exit codes—to eliminate false positives before filing a blocker report.

A comprehensive guide detailing advanced, authorized techniques for Active Directory security testing and red teaming. It covers critical attack vectors such as Kerberos exploitation, BloodHound path analysis, NTLM relay, and domain privilege escalation. Designed for security professionals to systematically map and exploit domain weaknesses.

A comprehensive skill designed to transform contested questions or complex decisions into a structured, auditable evidence map. It systematically tracks all supporting, contradicting, qualifying, and missing evidence, ensuring that reasoning relationships and source regions are preserved for rigorous review.

This guide outlines a structured, step-by-step onboarding process designed to establish trust and ensure user comfort. It systematically builds rapport, establishes administrative authority, and then guides the user to provide necessary credentials (like API keys and passwords) required to finalize the system setup. Ideal for initial user activation flows.

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 systematic methodology for resolving discrepancies between conceptual models (documentation, tests, diagrams, metrics) and actual observed behavior (live code execution or real data). This process mandates that observed reality (the 'territory') always overrules the theoretical map, ensuring system integrity and accurate knowledge updates.

A systematic methodology for solving complex, multi-domain, or high-stakes problems where a single mental model creates blind spots. This process guides users to select 2-3 complementary models, assign distinct roles, define a conflict rule, and synthesize findings into a single, decision-ready recommendation.

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 systematic planning technique that assumes a project or plan has already failed. By reasoning backward from a catastrophic outcome (in past tense), this method identifies the necessary conditions for failure. These necessary conditions are then inverted into concrete, verifiable preventive requirements, gates, and mitigation strategies, ensuring proactive risk management before execution begins.

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.