Skill UI

A comprehensive, multi-session framework designed to systematically uncover and articulate a brand’s core identity. Using advanced techniques like Laddering, 5 Whys, and Projective methods, the process covers purpose, positioning, audience, personality, and voice. The final output is a reusable Master Brandbook perfect for briefing designers and marketing teams.

This skill builds an active voice profile by extracting stylistic patterns, rhythms, and signatures from genuine source materials—such as posts, essays, and site copy. Instead of relying on generic AI tropes, it creates a reusable VOICE PROFILE block, ensuring consistent brand messaging across all content, social media, and outreach campaigns.

This system generates platform-native content for multiple channels, including X, LinkedIn, YouTube, and newsletters. It operates on a source-first workflow, ensuring that the author's unique voice and core ideas are preserved while adapting the content to the specific formatting and best practices of each target platform.

This skill simulates a four-voice council (Architect, Skeptic, Pragmatist, Critic) to resolve complex decisions marked by ambiguity or multiple trade-offs. It is designed for high-level strategic choices, identifying blind spots, and determining go/no-go decisions when a clear winner is absent. It specializes in synthesizing diverse perspectives to mitigate risk.

This skill manages the end-to-end operational workflow for corporate email communication. It is focused on the mechanics of the mailbox itself, allowing users to triage inboxes, draft replies, send messages, and verify successful delivery. It ensures that all actions are context-aware, adhering to brand voice, and providing verifiable proof of the message's final status (e.g., Sent or Drafted).

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.