doc-to-ai-solution

What problem does it solve?

Converts collections of meeting notes, technical docs, postmortems, and mixed document sets into a decision-ready engineering analysis that goes beyond summaries to produce one-line insights, root-cause judgments, buildable requirements, prioritized work, and an integrated AI solution design.

Core Features & Use Cases

• Evidence-first synthesis: extract and merge signals across documents into an evidence table that drives every conclusion.
• Problem → Requirements pipeline: convert observed symptoms into repeatable, task-shaped requirements with explicit Input, Output, and Acceptance criteria.
• AI integration design: propose concrete AI modules, data surfaces, and CI/CI or MR integration points rather than demo ideas.
• Use case: turn postmortems and incident notes into a prioritized set of fixes, observability requirements, and an AI-assisted triage pipeline ready for implementation.
• Feishu/Lark support: when sources live in Feishu/Lark, use the companion lark-doc skill to search and fetch documents before analysis.

Quick Start

Use the doc-to-ai-solution skill to read the provided documents and produce the fixed eight-section engineering output with evidence, task-shaped requirements, AI design, pipeline changes, priorities, and risks.