research-refine

To turn a vague research idea into an executable method plan, you provide an anchored problem and a rough direction, which the skill refines through phase-based, policy-guided iterations into a concrete proposal.

A problem anchor in research proposal planning explicitly defines the core problem to prevent scope drift. It is frozen and reused across every proposal and refinement round to ensure the final method plan remains focused and implementation-oriented.

To design a research method that avoids contribution sprawl, the refinement process favors the smallest adequate mechanism with a sharp, focus-driven contribution, rejecting boilerplate and ensuring an elegant, frontier-aware method plan.

You can resume an interrupted research planning session and trace revision history because the skill persists state and outputs to refine-logs, enabling resumable sessions and traceable lineage for your method design.

Yes, this approach works for early-stage PhD projects and foundation-model workflows by applying phase-driven refinement and literature grounding to transform vague directions into actionable, execution-ready plans.

Iterative AI review for research method design requires an explicitly defined problem anchor upfront and depends entirely on phase-based refinement rounds, meaning it cannot generate actionable plans from completely unanchored or undefined premises.