Decompose natural language requirements into Data, Rules, and Interactions.
npx skills add https://github.com/cruldra/skills --skill dri-text-analysis
Please help me install this Agent Skill. Skill: dri-text-analysis Source: https://github.com/cruldra/skills/tree/main/archived/dri-text-analysis Command: npx skills add https://github.com/cruldra/skills --skill dri-text-analysis
Extract structure from unstructured natural language requirements by decomposing them into Data, Rules, and Interactions. It produces a clear design vocabulary and a blueprint for subsequent architecture work.
Provide a natural language requirements document and have the tool return a DRI structured analysis.
High-intent search queries and answers about installing and using this skill.
To extract structured requirements from natural language, you decompose raw text into Data, Rules, and Interactions. This produces a clear domain vocabulary and actionable design artifacts for subsequent architecture work.
DRI decomposition in requirements engineering breaks down text into Data, Rules, and Interactions. It identifies domain language entities, constraints, and boundaries to generate architecture-ready artifacts and traceable development tasks.
Converting long requirement documents into actionable development tasks requires mapping DRI concepts to domain models, services, and interfaces. This creates traceable design artifacts that bridge the gap between raw text and implementation.
Yes, you can map extracted domain language concepts directly to architecture design models. The analysis translates DRI concepts into domain models, services, and interfaces, yielding a clear blueprint for subsequent architecture work.
No specific frameworks or dependencies are required to perform DRI text analysis. You simply provide a natural language requirements document, and the tool returns a structured DRI analysis without external component prerequisites.
You should avoid using DRI decomposition for requirements analysis when working with already structured data formats instead of unstructured natural language. It is specifically designed to extract structure from raw text, not to parse existing schemas.
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