What problem does it solve?
Research ideation often collapses onto the first plausible idea or produces vague inspiration that cannot survive novelty, feasibility, and manuscript-defensibility checks. This Skill turns an active baseline, codebase, and evaluation contract into concrete, testable, literature-grounded research directions with explicit tradeoffs.
Core Features & Use Cases
- Bounded divergent-convergent ideation: Generates 6-12 raw candidate ideas using deliberate ideation lenses, then converges to a serious frontier of 2-3 differentiated candidates.
- Mandatory literature grounding: Enforces a durable survey of at least 5-10 related papers with closest-prior-work tables and explicit novelty verdicts before any direction is promoted.
- Structured handoff: Produces durable artifacts (limitations analysis, literature survey, selected idea draft, outline seed) and a falsifiable claim with minimal experiment and abandonment condition for the experiment stage.
- Use Case: After establishing a baseline for a machine learning task, use this Skill to analyze failure modes, sweep arXiv for competing methods, rank candidate directions, and select one defensible research idea ready for experimentation.
Quick Start
Use the ds-idea skill to analyze the current baseline's limitations, survey related work, and select the next research direction for this quest.