Skill UI

JTBD framework for exploring customer jobs, pains, and gains to validate product ideas, prioritize roadmaps, sharpen messaging, and surface unmet needs before committing to solutions.

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 teams through Amazon's Working Backwards press release process to define customer value, align stakeholders on a new product or feature, and surface gaps before building.

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.

Generates a six-frame narrative storyboard that chronicles a user’s journey from problem to solution, helping teams align, pitch features, and emotionally validate ideas before committing to builds.

Guides product managers through TAM, SAM, and SOM calculations with adaptive questions, data citations, and context-aware prompts to build credible, investor-ready market size estimates for product ideas.

Guides product teams through adaptive prompts to build two-dimensional user story maps that capture backbone activities, user tasks, and release slices for clearer priorities and incremental release planning.

This skill implements the BDI (Belief-Desire-Intention) architecture for advanced cognitive agent modeling. It allows agents to reason about context by transforming external knowledge (like RDF triples) into internal mental states (beliefs, desires, intentions) and vice versa. Key features include goal-directed planning, explainable reasoning chains, and T2B2T (Triples-to-Beliefs-to-Triples) flow, making it ideal for multi-agent systems and augmenting LLMs with formal cognitive structures.

This skill simulates a senior engineer's final, deep audit of an entire codebase before committing or pushing to a remote repository. It systematically checks for junk files, dead code, security vulnerabilities (secrets, injection flaws), and performance bottlenecks (N+1 queries). Use this pre-flight check before any major release or public deployment to ensure maximum code quality and production readiness.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

Analyzes test coverage gaps by cataloging routes, components, APIs, and existing specs, then builds a coverage matrix, prioritizes missing areas, and recommends follow-up plans or auto-generation for missing tests.

Culture Architect turns company culture into observable behaviors, measurable health, and rituals that scale; run mission/vision/value workshops, translate values into concrete actions, build culture codes, assess cultural health, and design rituals whenever values, culture debt, or founder-driven culture need alignment.