Creates structured context documents that AI systems can consume to generate consistent, accurate content for a feature. Use when preparing a feature for AI-assisted content generation, building AI writing workflows, or enabling LLMs to write within product constraints.
Creates the canonical vocabulary for a feature, including term definitions, usage rules, and prohibited alternatives. Use when starting content work on a new feature, resolving terminology inconsistencies, or establishing naming conventions for concepts within a feature.
Compares context pack versions to identify changes, generates semantic diffs, and applies version control discipline. Use when updating context packs, reviewing changes before publish, auditing context drift, or maintaining context history.
Creates structured context packs that provide AI systems and content creators with complete information about a feature. Use when preparing for content generation, onboarding writers to features, or building AI-ready documentation.
Creates visual and structured representations of all possible states a feature can exist in, including transitions, triggers, and content requirements per state. Use when documenting a new feature, auditing existing state handling, or preparing context for UI copy.
Maps content touchpoints to user journey stages, identifying what content appears at each phase from awareness through advocacy. Use when planning content strategy for a feature, auditing content coverage, or identifying content gaps in the user experience.
Updates existing feature content context based on user feedback, research findings, or product changes. Use when feedback reveals gaps, inaccuracies, or shifts in user mental models within an existing context pack.
Validates that a context pack contains all required fields, consistent state coverage, and no conflicting definitions. Use when reviewing context packs before handoff, after major updates, or before AI integration.