Skill UI

This skill automatically cleans up and prettifies Markdown documents. It fixes structural inconsistencies such as incorrect heading levels and normalizes various list styles. It is useful for ensuring consistent formatting across large markdown files, optimizing readability, and preparing content for documentation or publishing.

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.

This utility provides a straightforward mechanism for reading the contents of specified files and returning the data. It is ideal for quick file inspection, data validation, or integrating basic file content retrieval into automated scripts and workflows.

A highly advanced AI assistant designed to create a deeply personalized experience by remembering every detail, preference, and piece of information shared by the user. It actively tracks conversations, weaving relevant personal memories and providing a comprehensive, verbatim summary into every response, ensuring continuity across sessions.

A utility designed to read the contents of local data files by specifying their file path. This tool is useful for quick data inspection, initial data loading, or preliminary analysis when a user needs to access raw information from various file formats (e.g., TXT, CSV).

This utility is designed to automatically generate professional, formatted reports from structured data sources. Users simply provide the designated data input, and the system handles the entire report generation process, ensuring high consistency and quality across all resulting documents. It is ideal for data analysts and business intelligence users who require standardized reporting.

This skill processes public Shopify App Store reviews (especially low-star ones) to generate a structured, actionable brief. It categorizes feedback into incident risk, repeated friction, pricing confusion, and feature requests, providing a prioritized P0-P3 assessment for product and support teams. It ensures every piece of feedback maintains its public source link and labeling for human review.

This robust methodology guides users to strip away assumed constraints, industry conventions, or historical precedents. By isolating only the fundamental, independently supported primitives (like physics, core math, or measured needs), it enables the rebuilding of the simplest, most robust solution that genuinely satisfies real-world constraints, bypassing artificial or arbitrary limitations.

This advanced methodology builds an evidence-linked causal chain from a localized fault to an actionable systemic root. It moves beyond simple proximate causes by requiring counterfactual testing, verifying multiple independent pathways, and prescribing countermeasures that eliminate enabling conditions, ensuring true recurrence prevention.

A strategic framework used in product management to shift focus from features to the core progress users are trying to achieve. It helps teams understand why users 'hire' a solution, map the forces driving change, and prioritize product development based on the true job needing completion, rather than just existing feature lists. Ideal for analyzing low adoption or defining new product strategy.

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.

This framework applies the Lindy Effect principle to assess the expected remaining life of non-perishable technologies, ideas, or practices. It treats the current survival age as a strong prior for future longevity, guiding users to favor proven, long-surviving options. It is crucial when making critical, high-stakes choices (like selecting frameworks or protocols) where relevance must last years, requiring checks for domain drift and a clear burden of proof before adopting novelty.