Skill UI

A powerful cognitive tool that moves beyond surface-level brainstorming. It forces users to systematically deconstruct complex problems down to their 'irreducible truths.' The skill identifies 8-12 hidden assumptions (Surface, Middle, Deep), classifies them (Fact, Convention, Belief, Interest-Driven), and ranks them by Fragility × Impact. This process allows users to stress-test their premises and rebuild conclusions based only on verified realities, making it invaluable for major life decisions or business hypothesis testing.

A multi-phase workflow designed to guide users through complex problem analysis and solution design. Instead of providing immediate answers, it systematically enforces deep thinking by reviewing the problem's validity, assessing information gaps, performing root cause analysis, and iterating through solution vetting (self-check and final review). Ideal for complex technical troubleshooting, decision-making, or theoretical discussions requiring rigorous methodology.

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

A comprehensive guide for connecting resource-constrained microcontrollers (like Arduino) to Azure IoT Hub and IoT Edge. It provides structured patterns for secure provisioning, handling intermittent networks (store-and-forward), robust telemetry transmission (MQTT over TLS), and implementing local edge decision-making for industrial and field deployments.

This tool serves as a Codex CLI alias implementing the P10 protocol, simulating the role of a P10/CTO. It is designed to assist users in defining macro-level strategic corporate direction, mapping complex organizational topologies, and establishing management boundaries for lower levels (P9). It is ideal for high-level strategic planning and decision-making simulation.

This skill provides an expert framework for applying psychological principles and mental models to marketing strategy. It helps users understand deep consumer behavior, solve complex decision-making problems, and ethically influence purchasing decisions. Use it when addressing cognitive biases, persuasion techniques, or needing foundational frameworks like First Principles and Jobs to Be Done for strategic optimization.

A comprehensive, 6-phase simulation of a C-suite boardroom meeting. This skill facilitates structured strategic deliberation across multiple executive domains (e.g., Finance, Product, Marketing). Key phases include independent thinking (isolation), cross-examination, and devil's advocate reviews. The output is a formal Board Memo detailing recommendations, vote tallies, explicit dissents, and critical risk assessments, ensuring robust decision-making.

This skill simulates a Chief Data Officer's deep interrogation, pressure-testing any plan touching core data strategy. Use it before approving ML model training runs, selecting data infrastructure (warehouse vs. mesh), productizing data assets, or making major team hires. It ensures that all data decisions are grounded in clear business decisions, compliance, and structural necessity.

Provides high-level strategic guidance for founders and Chief Data Officers (CDOs) in startups. This skill covers critical decisions such as AI training data rights and consent provenance, selecting optimal data architectures (warehouse, lakehouse, data mesh), valuing customer data as a strategic asset for M&A or fundraising, and planning data team organizational evolution. It focuses exclusively on strategic decision-making, not tactical engineering.

This tool simulates a Chief Technology Officer's comprehensive review, stress-testing any major technical plan. It systematically interrogates critical areas including scaling limits (scaling cliffs), accumulated technical debt, team capacity planning, build-vs-buy economics, system reliability (SLOs), and security compliance. Use it before committing to core architecture changes or making high-stakes engineering decisions.

A structured founder interview designed to gather the canonical, comprehensive data for a company, including financials, team structure, business metrics, and strategic risks. It generates a single, authoritative `company-context.md` file, serving as the single source of truth for all subsequent AI agents and strategic decision-making processes. Essential for initial setup or after major company life events like funding rounds or pivots.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.