ai-opportunities

Identify AI opportunities in workflow documents by mapping touchpoints to data sources, output constraints, and iteration budgets. This skill provides a structured blueprint that classifies AI interaction points and defines context engineering requirements for downstream rendering.

Context engineering for AI touchpoints is the process of defining data sources, hops, shapes, quality, risk, and output requirements for each AI interaction point. It ensures AI sections have consistent technical context pipelines and business impact framing.

Create a design doc for AI integration by using a dual-layer framework that separates business impact from technical context. Map each touchpoint to data pipelines, constraints, and design decisions to deliver a reusable blueprint for consumers.

Estimate iteration budgets for AI workflow features by justifying complexity per touchpoint. This skill provides guidance on mapping iteration estimates against data source requirements and output constraints to inform downstream technical design.

Yes, you can generate technical reference artifacts for AI workflows. The skill provides collapsible SA references for each touchpoint, including data pipelines, constraints, and design decisions to guide technical rendering.

Yes, the framework supports both business and technical layers for AI documentation. It delivers a dual-layer blueprint that frames business impact while detailing technical context pipelines and output constraints for each touchpoint.