Structure business goals and requirements across four vertical layers with one-way traceability.
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Please help me install this Agent Skill. Skill: rdra Source: https://github.com/iwasa-kosui/dotfiles/tree/main/dot_claude/skills/rdra Command: npx skills add https://github.com/iwasa-kosui/dotfiles --skill rdra-iwasa-kosui
RDRA 3.0-based requirements analysis helps teams structure business needs across four vertical layers (value, environment, boundary, system) and establish one-way traceability from GOAL to UCs using YAML-frontmatter Markdown artifacts.
Provide your project scope and goals, then follow the RDRA guide to craft overview.md, define contexts, and generate traceability artifacts.
High-intent search queries and answers about installing and using this skill.
RDRA 3.0 requirements analysis structures business goals across four vertical layers with one-way traceability from GOAL to UCs, generating YAML-frontmatter Markdown artifacts and traceability.yaml for end-to-end traceability.
Automated diagram generation for requirements modeling works by producing Mermaid diagrams and ER/state visuals directly from YAML frontmatter and context data, ensuring comprehensive traceability across the four vertical layers.
Yes, you can generate PRD and ADR documents from structured requirements analysis. The mode-driven workflow includes a Document Generation mode that turns defined contexts and shared models into formalized artifacts.
The best way to establish traceability from business goals to use cases is using a four vertical layer structure. This enforces one-way traceability from GOAL to UCs and outputs a traceability.yaml file mapping the relationships.
Yes, requirements analysis with YAML frontmatter supports updating existing artifacts. The mode-driven workflow includes an Update mode to align goals with concrete artifacts and apply changes across the RDRA repository.
You need a standardized ID schema for requirements documentation when your project requires formalized analysis and strict traceability. It enforces consistent artifact identification across overview.md, contexts, and shared models.
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