--- name: "idea-discovery" description: "Workflow 1: Full idea discovery pipeline. Orchestrates research-lit \u2192 idea-creator \u2192 novelty-check \u2192 research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says \\\"\u627eidea\u5168\u6d41\u7a0b\\\", \\\"idea discovery pipeline\\\", \\\"\u4ece\u96f6\u5f00\u59cb\u627e\u65b9\u5411\\\", or wants the complete idea exploration workflow." --- > Override for Codex users who want **Gemini**, not a second Codex agent, to act as the reviewer. Install this package **after** `skills/skills-codex/*`. # Workflow 1: Idea Discovery Pipeline Orchestrate a complete idea discovery workflow for: **$ARGUMENTS** ## Overview This skill chains sub-skills into a single automated pipeline: ``` /research-lit → /idea-creator → /novelty-check → /research-review → /research-refine-pipeline (survey) (brainstorm) (verify novel) (critical feedback) (refine method + plan experiments) ``` Each phase builds on the previous one's output. The final deliverables are a validated `idea-stage/IDEA_REPORT.md` with ranked ideas, plus a refined proposal (`refine-logs/FINAL_PROPOSAL.md`) and experiment plan (`refine-logs/EXPERIMENT_PLAN.md`) for the top idea. ## Constants - **PILOT_MAX_HOURS = 2** — Skip any pilot experiment estimated to take > 2 hours per GPU. Flag as "needs manual pilot" in the report. - **PILOT_TIMEOUT_HOURS = 3** — Hard timeout: kill any running pilot that exceeds 3 hours. Collect partial results if available. - **MAX_PILOT_IDEAS = 3** — Run pilots for at most 3 top ideas in parallel. Additional ideas are validated on paper only. - **MAX_TOTAL_GPU_HOURS = 8** — Total GPU budget across all pilots. If exceeded, skip remaining pilots and note in report. - **AUTO_PROCEED = true** — If user doesn't respond at a checkpoint, automatically proceed with the best option after presenting results. Set to `false` to always wait for explicit user confirmation. - **OUTPUT_DIR = `idea-stage/`** — All idea-stage outputs go here. Create the directory if it doesn't exist. - **REVIEWER_MODEL = `gemini-review`** — Gemini reviewer invoked through the local `gemini-review` MCP bridge. Passed to the reviewer-aware sub-skills installed by this overlay. - **ARXIV_DOWNLOAD = false** — When `true`, `/research-lit` downloads the top relevant arXiv PDFs during Phase 1. When `false` (default), only fetches metadata. Passed through to `/research-lit`. > 💡 These are defaults. Override by telling the skill, e.g., `/idea-discovery "topic" — pilot budget: 4h per idea, 20h total` or `/idea-discovery "topic" — arxiv download: true`. ## Pipeline ### Phase 1: Literature Survey Invoke `/research-lit` to map the research landscape: ``` /research-lit "$ARGUMENTS" ``` **What this does:** - Search arXiv, Google Scholar, Semantic Scholar for recent papers - Build a landscape map: sub-directions, approaches, open problems - Identify structural gaps and recurring limitations - Output a literature summary (saved to working notes) **🚦 Checkpoint:** Present the landscape summary to the user. Ask: ``` 📚 Literature survey complete. Here's what I found: - [key findings, gaps, open problems] Does this match your understanding? Should I adjust the scope before generating ideas? (If no response, I'll proceed with the top-ranked direction.) ``` - **User approves** (or no response + AUTO_PROCEED=true) → proceed to Phase 2 with best direction. - **User requests changes** (e.g., "focus more on X", "ignore Y", "too broad") → refine the search with updated queries, re-run `/research-lit` with adjusted scope, and present again. Repeat until the user is satisfied. ### Phase 2: Idea Generation + Filtering + Pilots Invoke `/idea-creator` with the landscape context: ``` /idea-creator "$ARGUMENTS" ``` **What this does:** - Brainstorm 8-12 concrete ideas via the Gemini-backed `/idea-creator` overlay - Filter by feasibility, compute cost, quick novelty search - Deep validate top ideas (full novelty check + devil's advocate) - Run parallel pilot experiments on available GPUs (top 2-3 ideas) - Rank by empirical signal - Output `idea-stage/IDEA_REPORT.md` **🚦 Checkpoint:** Present `idea-stage/IDEA_REPORT.md` ranked ideas to the user. Ask: ``` 💡 Generated X ideas, filtered to Y, piloted Z. Top results: 1. [Idea 1] — Pilot: POSITIVE (+X%) 2. [Idea 2] — Pilot: WEAK POSITIVE (+Y%) 3. [Idea 3] — Pilot: NEGATIVE, eliminated Which ideas should I validate further? Or should I regenerate with different constraints? (If no response, I'll proceed with the top-ranked ideas.) ``` - **User picks ideas** (or no response + AUTO_PROCEED=true) → proceed to Phase 3 with top-ranked ideas. - **User unhappy with all ideas** → collect feedback ("what's missing?", "what direction do you prefer?"), update the prompt with user's constraints, and re-run Phase 2 (idea generation). Repeat until the user selects at least 1 idea. - **User wants to adjust scope** → go back to Phase 1 with refined direction. ### Phase 3: Deep Novelty Verification For each top idea (positive pilot signal), run a thorough novelty check: ``` /novelty-check "[top idea 1 description]" /novelty-check "[top idea 2 description]" ``` **What this does:** - Multi-source literature search (arXiv, Scholar, Semantic Scholar) - Cross-verify with the Gemini-backed `/novelty-check` overlay - Check for concurrent work (last 3-6 months) - Identify closest existing work and differentiation points **Update `idea-stage/IDEA_REPORT.md`** with deep novelty results. Eliminate any idea that turns out to be already published. ### Phase 4: External Critical Review For the surviving top idea(s), get brutal feedback: ``` /research-review "[top idea with hypothesis + pilot results]" ``` **What this does:** - Gemini acts as a senior reviewer (NeurIPS/ICML level) via the local `gemini-review` MCP bridge - Scores the idea, identifies weaknesses, suggests minimum viable improvements - Provides concrete feedback on experimental design **Update `idea-stage/IDEA_REPORT.md`** with reviewer feedback and revised plan. ### Phase 4.5: Method Refinement + Experiment Planning After review, refine the top idea into a concrete proposal and plan experiments: ``` /research-refine-pipeline "[top idea description + pilot results + reviewer feedback]" ``` **What this does:** - Freeze a **Problem Anchor** to prevent scope drift - Iteratively refine the method via Gemini review (up to 5 rounds, until score ≥ 9) - Generate a claim-driven experiment roadmap with ablations, budgets, and run order - Output: `refine-logs/FINAL_PROPOSAL.md`, `refine-logs/EXPERIMENT_PLAN.md`, `refine-logs/EXPERIMENT_TRACKER.md` **🚦 Checkpoint:** Present the refined proposal summary: ``` 🔬 Method refined and experiment plan ready: - Problem anchor: [anchored problem] - Method thesis: [one sentence] - Dominant contribution: [what's new] - Must-run experiments: [N blocks] - First 3 runs to launch: [list] Proceed to implementation? Or adjust the proposal? ``` - **User approves** (or AUTO_PROCEED=true) → proceed to Final Report. - **User requests changes** → pass feedback to `/research-refine` for another round. - **Lite mode:** If reviewer score < 6 or pilot was weak, run `/research-refine` only (skip `/experiment-plan`) and note remaining risks in the report. ### Phase 5: Final Report Finalize `idea-stage/IDEA_REPORT.md` with all accumulated information: ```markdown # Idea Discovery Report **Direction**: $ARGUMENTS **Date**: [today] **Pipeline**: research-lit → idea-creator → novelty-check → research-review → research-refine-pipeline ## Executive Summary [2-3 sentences: best idea, key evidence, recommended next step] ## Literature Landscape [from Phase 1] ## Ranked Ideas [from Phase 2, updated with Phase 3-4 results] ### 🏆 Idea 1: [title] — RECOMMENDED - Pilot: POSITIVE (+X%) - Novelty: CONFIRMED (closest: [paper], differentiation: [what's different]) - Reviewer score: X/10 - Next step: implement full experiment → /auto-review-loop ### Idea 2: [title] — BACKUP ... ## Eliminated Ideas [ideas killed at each phase, with reasons] ## Refined Proposal - Proposal: `refine-logs/FINAL_PROPOSAL.md` - Experiment plan: `refine-logs/EXPERIMENT_PLAN.md` - Tracker: `refine-logs/EXPERIMENT_TRACKER.md` ## Next Steps - [ ] /run-experiment to deploy experiments from the plan - [ ] /auto-review-loop to iterate until submission-ready - [ ] Or invoke /research-pipeline for the complete end-to-end flow ``` ## 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. - **Don't skip phases.** Each phase filters and validates — skipping leads to wasted effort later. - **Checkpoint between phases.** Briefly summarize what was found before moving on. - **Kill ideas early.** It's better to kill 10 bad ideas in Phase 3 than to implement one and fail. - **Empirical signal > theoretical appeal.** An idea with a positive pilot outranks a "sounds great" idea without evidence. - **Document everything.** Dead ends are just as valuable as successes for future reference. - **Be honest with the reviewer.** Include negative results and failed pilots in the review prompt. - **Feishu notifications are optional.** If `~/.codex/feishu.json` exists, send `checkpoint` at each phase transition and `pipeline_done` at final report. If absent/off, skip silently. ## Composing with Workflow 2 After this pipeline produces a validated top idea: ``` /idea-discovery "direction" ← you are here (Workflow 1, includes method refinement + experiment planning) /run-experiment ← deploy experiments from the plan /auto-review-loop "top idea" ← Workflow 2: iterate until submission-ready Or use /research-pipeline for the full end-to-end flow. ```