What problem does it solve? In a multi-skill research system, deciding which skills to invoke, in what order, and under which execution mode is error-prone. This Skill acts as the system entry point: it analyzes each user query, classifies its complexity, selects an execution mode (standard chain, exploratory loop, research double-loop, or parallel), and dispatches work to the correct cognitive atoms while logging every decision. ## Core Features & Use Cases - Query-to-Mode Routing: Classifies queries into standard chain, exploratory loop, research double-loop, or parallel execution, then selects the matching atom chain (ACQ→EXT→ASC→HYP→ARG→VER) without skipping or over-invoking steps. - Sub-Agent Delegation Discipline: Enforces delegate_task rules—pass the user's original goal with an empty context, never micro-manage sub-agents, and split batches of more than 10 papers into parallel subtasks. - Traceable Execution: Creates outputs/{session_id}/pipeline_trace.json recording mode, chain, per-atom status, loop state, and gene activation metadata for reproducibility. - Use Case: A user asks to "search 3D nystagmus literature"; the router selects the standard chain (knowledge-acquisition → knowledge-extraction), creates the pipeline trace, and delegates the task verbatim to a sub-agent. ## Quick Start Ask the agent to route the query "search 3D nystagmus literature" and verify that pipeline_trace.json records a standard route with the knowledge-acquisition and knowledge-extraction atoms.