skill-context-detection

Context detection automatically classifies tasks into development or knowledge modes using prompt signals, project files, and user overrides. This eliminates manual workflow switching by identifying your work context and adapting orchestration behavior accordingly for adaptive research, building, review, and delivery processes.

Task classification relies on prompt signals, project structures, and optional explicit user overrides to determine the appropriate context. By analyzing these inputs, the system detects whether a task requires development or knowledge-oriented processes to select matching agents and outputs.

Use context detection when orchestrating AI workflows that require adaptive research, building, review, and delivery processes. It is necessary when your tasks shift between development contexts, like API implementation, and knowledge contexts, such as market strategy reports, requiring different workflow behaviors.

Yes, explicit user overrides are supported alongside prompt signals and project files for context classification. Manual overrides ensure the workflow adaptation aligns with your specific intent, even if the automated task classification suggests a different development or knowledge mode.

Context detection can classify tasks using prompt signals alone, but analyzing project files improves accuracy. Optional user overrides provide a fallback mechanism, ensuring workflow adaptation functions correctly even when structural project context is unavailable.