What problem does it solve? Launching an autonomous Idea Tree research run requires well-prepared objectives, constraints, and evidence; this Skill ensures the Lead gathers literature and supplied materials first, then correctly hands off execution to the Python research engine instead of simulating the tree manually. ## Core Features & Use Cases - Evidence-first preparation: Searches and reads relevant literature, then summarizes evidence with source references, constraints, and uncertainties before submission. - Engine handoff: Calls create_idea_research(objective, materials) so the Python engine owns all tree iterations, assessments, and insight propagation. - Result explanation: Uses get_idea_research to explain saved results in terms of candidate differences, assessment findings, improvements, and uncertainties. - Use Case: A materials researcher asks to explore catalyst hypotheses; the Skill retrieves supporting literature, packages the objective and evidence, starts the Idea Tree engine, and later explains the scored leaf candidates. ## Quick Start Ask the agent to run /idea-tree-team on your research question so it gathers supporting literature and starts an autonomous Idea Tree research run.
