idea-generationlisted

# Idea Generation Generate and refine novel research ideas with literature-backed novelty assessment. ## Input - `$0` — Research area, task description, or existing codebase context - `$1` — Optional: additional context (e.g., "for NeurIPS", constraints) ## Scripts ### Novelty check against Semantic Scholar ```bash python ~/.claude/skills/idea-generation/scripts/novelty_check.py \ --idea "Adaptive attention head pruning via gradient-guided importance" \ --max-rounds 5 ``` Performs iterative literature search to assess if an idea is novel. ## References - Ideation prompts (generation, reflection, novelty): `~/.claude/skills/idea-generation/references/ideation-prompts.md` ## Workflow ### Step 1: Generate Ideas Given a research area and optional code/paper context: 1. Generate 3-5 diverse research ideas 2. For each idea, provide: Name, Title, Experiment plan, and ratings 3. Use the ideation prompt templates from references ### Step 2: Iterative Refinement (up to 5 rounds per idea) For each idea: 1. Critically evaluate quality, novelty, and feasibility 2. Refine the idea while preserving its core spirit 3. Stop when converged ("I am done") or max rounds reached ### Step 3: Novelty Assessment For each promising idea: 1. Run `novelty_check.py` or manually search Semantic Scholar / arXiv 2. Use the novelty checking prompts from references 3. Multi-round search: generate queries, review results, decide 4. Binary decision: Novel / Not Novel with justification ### Step