--- name: "idea-creator" description: "Generate and rank research ideas given a broad direction. Use when user says \"\u627eidea\", \"brainstorm ideas\", \"generate research ideas\", \"what can we work on\", or wants to explore a research area for publishable directions." --- > Override for Codex users who want **Gemini**, not a second Codex agent, to act as the reviewer. Install this package **after** `skills/skills-codex/*`. # Research Idea Creator > **Gemini overlay assurance:** `review_independence: cross-family` and `acceptance_status: accepted`. Generate publishable research ideas for: $ARGUMENTS ## Overview Given a broad research direction from the user, systematically generate, validate, and rank concrete research ideas. Standalone, Phase 1's landscape survey is **inline** (WebSearch — it does not invoke `/research-lit`); Phases 4-5 invoke `/novelty-check`, `/run-experiment`, and `/monitor-experiment` for validation and pilots. For the full sub-skill pipeline (`/research-lit` → idea generation → `/novelty-check` → `/research-review`), run `/idea-discovery` (Workflow 1), which orchestrates this skill. ## 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 = `gemini-review`** — Gemini reviewer invoked through the local `gemini-review` MCP bridge for brainstorming and critique. Set `GEMINI_REVIEW_MODEL` if you need a specific Gemini model override. - **OUTPUT_DIR = `idea-stage/`** — Directory for idea output files. > 💡 Override via argument, e.g., `/idea-creator "topic" — pilot budget: 4h per idea, 20h total`. ## Workflow ### 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 local `gemini-review` MCP bridge for divergent thinking: ``` mcp__gemini-review__review_start: 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) - Simple at the core: one mechanism, few moving parts — an idea a colleague could restate after hearing it once. If the novelty only appears once a second module or an extra gate is added, that is packaging, not novelty. - Aware of the 10-15 papers above — awareness, not avoidance. Differentiation is the novelty check's job later, not a constraint on brainstorming. "Apply X to Y" is legitimate when the application would reveal something non-obvious — judge it by what it reveals, not by the template. A direct, well-executed attack on a central problem is a valid idea when nobody has executed it well; do not steer around crowded areas — proximity to strong work is a sign the problem matters, not that it is taken. Generate first, filter later — the filters come after you, and they are strict enough. A bold, simple idea with a named risk beats a hedged, complicated one with none. A great idea is one where the answer matters regardless of which way it goes. ``` After this start call, immediately save the returned `jobId` and poll `mcp__gemini-review__review_status` with a bounded `waitSeconds` until `done=true`. Treat the completed status payload's `response` as the brainstorm output, and save the completed `threadId` for follow-up critique in Phase 4. ### Phase 3: Mechanical consolidation + objective feasibility gate > This phase does NOT judge idea quality, novelty, or impact — those are the > job of the Phase-4 cross-model reviewer (a different model family). Dropping > ideas here on a same-family novelty or impact call would pre-filter the > reviewer's input with same-family judgment — the opposite of why ARIS uses a > cross-model reviewer at all. Phase 3 only (a) clusters near-duplicate ideas > and (b) drops ideas that are OBJECTIVELY out of budget; everything else > passes through ANNOTATED, not eliminated. 1. **Objective feasibility gate (safe to gate here)**: drop an idea ONLY on a mechanical, budget-based fact — estimated compute > 1 week of available GPU time, OR a dataset that is provably unavailable. Do NOT drop on "implementation looks complex" — annotate complexity instead. 2. **Novelty signal — ANNOTATE, do not eliminate**: do 2-3 targeted searches and attach a `prior_work` note (what looks related, with links). This is input for the Phase-4 reviewer, not a filter; full `/novelty-check` runs in Phase 4. Do NOT drop an idea here because it "might already be done." 3. **Impact signal — ANNOTATE, do not eliminate**: attach a one-line `so_what` note (why the result would matter either way). Do NOT drop on a same-family "a reviewer wouldn't care" call — that is exactly what the Phase-4 cross-model reviewer is for. Every feasible, non-duplicate idea — with its `prior_work` and `so_what` annotations — proceeds to Phase 4, where the cross-model reviewer does the quality/novelty narrowing. ### Phase 4: Deep Validation (for top ideas) For each surviving idea, run a deeper evaluation: 1. **Novelty check**: Use the `/novelty-check` workflow (multi-source search + Gemini cross-verification) for each idea 2. **Critical review**: Use `mcp__gemini-review__review_reply_start` with the saved completed `threadId`: ``` mcp__gemini-review__review_reply_start: threadId: [saved completed threadId from Phase 2] prompt: | Here are our top ideas after filtering: [paste surviving ideas with novelty check results] For each, make the strongest case both ways: - What is the best case FOR it — what would make this the paper people cite? - 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? Rank; do not rewrite. An objection is answered or recorded as a named risk on the idea — never absorbed by adding a module, a gate, or a qualifier. A bold idea with a named risk outranks a hedged idea with none, and complexity added since the brainstorm is a red flag, not progress. ``` After this start call, immediately save the returned `jobId` and poll `mcp__gemini-review__review_status` with a bounded `waitSeconds` until `done=true`. Treat the completed status payload's `response` as the follow-up critique. 3. **Combine rankings**: Merge your assessment with Gemini'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 `/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 `/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-stage/IDEA_REPORT.md`: **Lead every recommended idea with its method, in plain language.** Before any hypothesis, novelty score, or claim, state in 2–4 concrete steps what we actually build / train / run — no jargon, no claim-IDs. The reader must understand *what we do* before *what we claim*; claims (hypothesis, validation, expected outcome) come after and read as the method's acceptance criteria. ```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] - **Method (what we actually do)**: [2–4 concrete steps in plain language — what we build / train / run. No jargon, no claim-IDs, no hypothesis yet. Lead with this so the reader grasps the approach first.] - **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 /auto-review-loop for full iteration ``` ## Output Protocols > Follow these shared protocols for all output files: > - **[Output Versioning Protocol](../../shared-references/output-versioning.md)** — write timestamped file first, then copy to fixed name > - **[Output Manifest Protocol](../../shared-references/output-manifest.md)** — log every output to MANIFEST.md > - **[Output Language Protocol](../../shared-references/output-language.md)** — respect the project's language setting ## 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: ``` /idea-creator "direction" → ranked ideas /novelty-check "top idea" → deep novelty verification (already done in Phase 4, but user can re-run) /research-review "top idea" → external critical feedback implement → write code /run-experiment → deploy to GPU /auto-review-loop → iterate until submission-ready ```