Skill UI

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Guides product managers from stakeholder requests to structured Opportunity Solution Trees by extracting outcomes, enumerating opportunities, mapping solutions, and evaluating experiments to select the best proof of concept before building.

Guide teams through a structured PESTEL assessment of external political, economic, social, technological, environmental, and legal forces to surface risks and opportunities for product strategy, market entry, and long-term planning.

Runs a structured positioning workshop that surfaces target customers, unmet needs, category, benefits, and differentiation so product teams align on strategy before drafting PRDs or launch plans.

Guides product managers from discovery notes to an aligned PRD by orchestrating problem framing, user context, solution definition, and success criteria so engineering and stakeholders share a clear source of truth.

Guides product teams through MITRE's Problem Framing Canvas with structured inward/outward/reframe questions so you clarify assumptions, surface stakeholders, and create an actionable HMW statement before ideating.

Guides product managers through strategic roadmap planning by orchestrating prioritization, epic definition, stakeholder alignment, and release sequencing into a structured workflow that turns strategy into executable outcomes and keeps stakeholders aligned.

Guides users who are unsure where to start by asking structured questions, then recommends the best matching skill(s) from the installed library with explanations and invocation templates.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This protocol defines communication standards for C-suite AI agents, enabling structured queries and responses between roles like CFO, CTO, and CEO. It includes syntax for invocations, response formats, and strict rules to prevent infinite loops and circular reasoning. Ideal for orchestrating multi-agent teams in enterprise settings, ensuring coordinated cross-functional analysis and safe board meeting simulations without chaos.

This skill guides the user through a structured process of requirement clarification before any development work begins. It ensures that all key details—including objectives, scope, constraints, and acceptance criteria—are explicitly defined. It prevents costly rework by forcing a pause and systematically identifying 'must-have' missing information.

This skill orchestrates a structured six-phase board meeting protocol using multiple AI agents to simulate C-suite executives. It prevents groupthink by ensuring independent contributions before synthesis, covering context loading, critic analysis, and founder review. Ideal for strategic decision-making, market expansion, product direction, and organizational planning. It extracts clean, actionable decisions and maintains a two-layer decision memory system for long-term context.