--- name: Research Idea Creator slug: research-idea-creator category: AI Engineering description: Research Idea Creator generates, validates, and ranks publishable research ideas from a broad topic. Use it when you need brainstormed directions, quick novelty checks, and a shortlist of ideas to test. github: "https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-idea-creator" language: JavaScript stars: 1047 forks: 116 install: "npx degit https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-idea-creator ~/.claude/skills/aris-idea-creator" installs_to: ~/.claude/skills/aris-idea-creator source_path: skills/aris-idea-creator/SKILL.md collection_size: 25 category_size: 2451 collection_url: "https://dirskills.com/collections/OpenLAIR/dr-claw" added: 2026-08-21T05:14:01.571Z last_synced: 2026-08-21T05:14:01.571Z canonical_url: "https://dirskills.com/skills/research-idea-creator" --- # Research Idea Creator Research Idea Creator generates, validates, and ranks publishable research ideas from a broad topic. Use it when you need brainstormed directions, quick novelty checks, and a shortlist of ideas to test. **Install:** ```bash npx degit https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-idea-creator ~/.claude/skills/aris-idea-creator ``` ## README # Research Idea Creator Generate publishable research ideas for: $ARGUMENTS ## Overview Given a broad research direction from the user, systematically generate, validate, and rank concrete research ideas. This skill composes with `/aris-research-lit`, `/aris-novelty-check`, and `/aris-research-review` to form a complete idea discovery pipeline. ## Constants - **PILOT_MAX_HOURS = 2** — Skip any pilot estimated to take > 2 hours per GPU. Flag as "needs manual pilot". - **PILOT_TIMEOUT_HOURS = 3** — Hard timeout: kill pilots exceeding 3 hours. Collect partial results if available. - **MAX_PILOT_IDEAS = 3** — Pilot at most 3 ideas in parallel. Additional ideas are validated on paper only. - **MAX_TOTAL_GPU_HOURS = 8** — Total GPU budget for all pilots combined. - **REVIEWER_MODEL = `gpt-5.4`** — Model used via Codex MCP for brainstorming and review. Must be an OpenAI model (e.g., `gpt-5.4`, `o3`, `gpt-4o`). > 💡 Override via argument, e.g., `/aris-idea-creator "topic" — pilot budget: 4h per idea, 20h total`. ## Workflow ### Phase 0: Load Research Wiki (if active) **Skip this phase entirely if `research-wiki/` does not exist.** ``` if research-wiki/query_pack.md exists AND is less than 7 days old: Read query_pack.md and use it as initial landscape context: - Treat listed gaps as priority search seeds - Treat failed ideas as a banlist (do NOT regenerate similar ideas) - Treat top papers as known prior work (do not re-search them) Still run Phase 1 below for papers from the last 3-6 months (wiki may be stale) else if research-wiki/ exists but query_pack.md is stale or missing: python3 tools/research_wiki.py rebuild_query_pack research-wiki/ Then read query_pack.md as above ``` ### Phase 1: Landscape Survey (5-10 min) Map the research area to understand what exists and where the gaps are. 1. **Scan local paper library first**: Check `papers/` and `literature/` in the project directory for existing PDFs. Read first 3 pages of relevant papers to build a baseline understanding before searching online. This avoids re-discovering what the user already knows. 2. **Search recent literature** using WebSearch: - Top venues in the last 2 years (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.) - Recent arXiv preprints (last 6 months) - Use 5+ different query formulations - Read abstracts and introductions of the top 10-15 papers 2. **Build a landscape map**: - Group papers by sub-direction / approach - Identify what has been tried and what hasn't - Note recurring limitations mentioned in "Future Work" sections - Flag any open problems explicitly stated by multiple papers 3. **Identify structural gaps**: - Methods that work in domain A but haven't been tried in domain B - Contradictory findings between papers (opportunity for resolution) - Assumptions that everyone makes but nobody has tested - Scaling regimes that haven't been explored - Diagnostic questions that nobody has asked ### Phase 2: Idea Generation (brainstorm with external LLM) Use the external LLM via Codex MCP for divergent thinking: ``` mcp__codex__codex: model: REVIEWER_MODEL config: {"model_reasoning_effort": "xhigh"} prompt: | You are a senior ML researcher brainstorming research ideas. Research direction: [user's direction] Here is the current landscape: [paste landscape map from Phase 1] Key gaps identified: [paste gaps from Phase 1] Generate 8-12 concrete research ideas. For each idea: 1. One-sentence summary 2. Core hypothesis (what you expect to find and why) 3. Minimum viable experiment (what's the cheapest way to test this?) 4. Expected contribution type: empirical finding / new method / theoretical result / diagnostic 5. Risk level: LOW (likely works) / MEDIUM (50-50) / HIGH (speculative) 6. Estimated effort: days / weeks / months Prioritize ideas that are: - Testable with moderate compute (8x RTX 3090 or less) - Likely to produce a clear positive OR negative result (both are publishable) - Not "apply X to Y" unless the application reveals genuinely surprising insights - Differentiated from the 10-15 papers above Be creative but grounded. A great idea is one where the answer matters regardless of which way it goes. ``` Save the threadId for follow-up. ### Phase 3: First-Pass Filtering For each generated idea, quickly evaluate: 1. **Feasibility check**: Can we actually run this experiment with available resources? - Compute requirements (estimate GPU-hours) - Data availability - Implementation complexity - Skip ideas requiring > 1 week of GPU time or unavailable datasets 2. **Novelty quick-check**: For each idea, do 2-3 targeted searches to see if it's already been done. Full `/aris-novelty-check` comes later for survivors. 3. **Impact estimation**: Would a reviewer care about the result? - "So what?" test: if the experiment succeeds, does it change how people think? - Is the finding actionable or just interesting? Eliminate ideas that fail any of these. Typically 8-12 ideas reduce to 4-6. ### Phase 4: Deep Validation (for top ideas) For each surviving idea, run a deeper evaluation: 1. **Novelty check**: Use the `/aris-novelty-check` workflow (multi-source search + GPT-5.4 cross-verification) for each idea 2. **Critical review**: Use GPT-5.4 via `mcp__codex__codex-reply` (same thread): ``` Here are our top ideas after filtering: [paste surviving ideas with novelty check results] For each, play devil's advocate: - What's the strongest objection a reviewer would raise? - What's the most likely failure mode? - How would you rank these for a top venue submission? - Which 2-3 would you actually work on? ``` 3. **Combine rankings**: Merge your assessment with GPT-5.4's ranking. Select top 2-3 ideas for pilot experiments. ### Phase 5: Parallel Pilot Experiments (for top 2-3 ideas) Before committing to a full research effort, run cheap pilot experiments to get empirical signal. This is the key differentiator from paper-only validation. 1. **Design pilots**: For each top idea, define the minimal experiment that would give a positive or negative signal: - Single seed, small scale (e.g., small dataset subset, fewer epochs) - Target: 30 min - PILOT_MAX_HOURS per pilot on 1 GPU - **Estimate GPU-hours BEFORE launching.** If estimated time > PILOT_MAX_HOURS, reduce scale (fewer epochs, smaller subset) or flag as "needs manual pilot" - Clear success metric defined upfront (e.g., "if metric improves by > 1%, signal is positive") 2. **Deploy in parallel**: Use `/aris-run-experiment` to launch pilots on different GPUs simultaneously: ``` GPU 0: Pilot for Idea 1 GPU 1: Pilot for Idea 2 GPU 2: Pilot for Idea 3 ``` Use `run_in_background: true` to launch all at once. 3. **Collect results**: Use `/aris-monitor-experiment` to check progress. If any pilot exceeds PILOT_TIMEOUT_HOURS, kill it and collect partial results. Once all pilots complete (or timeout), compare: - Which ideas showed positive signal? - Which showed null/negative results? (eliminate or deprioritize) - Any surprising findings that suggest a pivot? - Total GPU-hours consumed (track against MAX_TOTAL_GPU_HOURS budget) 4. **Re-rank based on empirical evidence**: Update the idea ranking using pilot results. An idea with strong pilot signal jumps ahead of a theoretically appealing but untested idea. Note: Skip this phase if the ideas are purely theoretical or if no GPU is available. Flag skipped ideas as "needs pilot validation" in the report. ### Phase 6: Output — Ranked Idea Report Write a structured report to `IDEA_REPORT.md` in the project root: ```markdown # Research Idea Report **Direction**: [user's research direction] **Generated**: [date] **Ideas evaluated**: X generated → Y survived filtering → Z piloted → W recommended ## Landscape Summary [3-5 paragraphs on the current state of the field] ## Recommended Ideas (ranked) ### Idea 1: [title] - **Hypothesis**: [one sentence] - **Minimum experiment**: [concrete description] - **Expected outcome**: [what success/failure looks like] - **Novelty**: X/10 — closest work: [paper] - **Feasibility**: [compute, data, implementation estimates] - **Risk**: LOW/MEDIUM/HIGH - **Contribution type**: empirical / method / theory / diagnostic - **Pilot result**: [POSITIVE: metric +X% / NEGATIVE: no signal / SKIPPED: needs GPU] - **Reviewer's likely objection**: [strongest counterargument] - **Why we should do this**: [1-2 sentences] ### Idea 2: [title] ... ## Eliminated Ideas (for reference) | Idea | Reason eliminated | |------|-------------------| | ... | Already done by [paper] | | ... | Requires > 1 week GPU time | | ... | Result wouldn't be interesting either way | ## Pilot Experiment Results | Idea | GPU | Time | Key Metric | Signal | |------|-----|------|------------|--------| | Idea 1 | GPU 0 | 45 min | +2.3% CE | POSITIVE | | Idea 2 | GPU 1 | 30 min | -0.1% CE | NEGATIVE | | Idea 3 | GPU 2 | 1.5 hr | +0.8% CE | WEAK POSITIVE | ## Suggested Execution Order 1. Start with Idea 1 (positive pilot signal, lowest risk) 2. Idea 3 as backup (weak signal, may need larger scale to confirm) 3. Idea 2 eliminated by pilot — negative result documented ## Next Steps - [ ] Scale up Idea 1 to full experiment (multi-seed, full dataset) - [ ] If confirmed, invoke /aris-auto-review-loop for full iteration ``` ## Phase 7: Write Ideas to Research Wiki (if active) **Skip this phase entirely if `research-wiki/` does not exist.** This is critical for spiral learning — without it, `ideas/` stays empty and re-ideation has no memory. ``` if research-wiki/ exists: for each idea in recommended_ideas + eliminated_ideas: 1. Create page: research-wiki/ideas/.md - node_id: idea: - stage: proposed (or: piloted, archived) - outcome: unknown (or: negative, mixed, positive) - based_on: [paper:, ...] - target_gaps: [gap:, ...] - Include: hypothesis, proposed method, expected outcome - If pilot was run: actual outcome, failure notes, reusable components 2. Add edges: python3 tools/research_wiki.py add_edge research-wiki/ --from "idea:" --to "paper:" --type inspired_by --evidence "..." python3 tools/research_wiki.py add_edge research-wiki/ --from "idea:" --to "gap:" --type addresses_gap --evidence "..." Rebuild query pack: python3 tools/research_wiki.py rebuild_query_pack research-wiki/ Log: python3 tools/research_wiki.py log research-wiki/ "idea-creator wrote N ideas (M recommended, K eliminated)" ``` ## Key Rules - **Large file handling**: If the Write tool fails due to file size, immediately retry using Bash (`cat << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently. - The user provides a DIRECTION, not an idea. Your job is to generate the ideas. - Quantity first, quality second: brainstorm broadly, then filter ruthlessly. - A good negative result is just as publishable as a positive one. Prioritize ideas where the answer matters regardless of direction. - Don't fall in love with any idea before validating it. Be willing to kill ideas. - Always estimate compute cost. An idea that needs 1000 GPU-hours is not actionable for most researchers. - "Apply X to Y" is the lowest form of research idea. Push for deeper questions. - Include eliminated ideas in the report — they save future time by documenting dead ends. - **If the user's direction is too broad (e.g., "NLP", "computer vision", "reinforcement learning"), STOP and ask them to narrow it.** A good direction is 1-2 sentences specifying the problem, domain, and constraint — e.g., "factorized gap in discrete diffusion LMs" or "sample efficiency of offline RL with image observations". Without sufficient specificity, generated ideas will be too vague to run experiments on. ## Composing with Other Skills After this skill produces the ranked report: ``` /aris-idea-creator "direction" → ranked ideas /aris-novelty-check "top idea" → deep novelty verification (already done in Phase 4, but user can re-run) /aris-research-review "top idea" → external critical feedback implement → write code /aris-run-experiment → deploy to GPU /aris-auto-review-loop → iterate until submission-ready ```