knowledge-discovery

To discover and connect relevant skills for a multi-step task, this Skill identifies and prioritizes necessary knowledge using a tiered discovery framework, applying inline references and context graphs to map dependencies and structure workflows without relying on external tools.

Tiered discovery is a framework that matches task complexity through four levels: SKIP, QUICK, STANDARD, and DEEP. It provides scalable discovery by adjusting the depth of skill identification and prioritization based on whether the task is a simple edit or a full-feature implementation.

You can map out required skills for a CRUD interface by applying the Skill's context graph to identify dependencies and inline references. This structured workflow approach ensures you connect the most relevant knowledge needed for the specific multi-step implementation.

Yes, you can use this knowledge discovery approach for full-feature implementations. The tiered DEEP discovery level is specifically designed to handle complex, multi-step tasks by thoroughly identifying and connecting all relevant skills and dependencies required for the complete feature build.

No, this skill discovery framework does not require external tools or dependencies. It operates independently by leveraging a layered approach with SKILL-INDEX and CONTEXT-GRAPH to provide quick guidance, scalable discovery, and structured workflows internally.

The best way to reduce cognitive load when searching for related knowledge is using a tiered discovery framework with context graphs. This approach rapidly identifies and connects the most relevant skills, reducing search time by providing structured workflows and inline references.