Workflow 1: Idea Discovery Pipeline

Orchestrate a complete idea discovery workflow for: $ARGUMENTS

Overview

This skill chains four sub-skills into a single automated pipeline:

/research-lit → /idea-creator → /novelty-check → /research-review
  (survey)      (brainstorm)    (verify novel)    (critical feedback)

Each phase builds on the previous one's output. The final deliverable is a validated IDEA_REPORT.md with ranked ideas, pilot results, and a suggested execution plan.

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.
REVIEWER_MODEL = gpt-5.4 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.4, o3, gpt-4o). Passed to sub-skills.
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 GPT-5.4 xhigh
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_REPORT.md

🚦 Checkpoint: Present 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 GPT-5.4 xhigh
Check for concurrent work (last 3-6 months)
Identify closest existing work and differentiation points

Update 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:

GPT-5.4 xhigh acts as a senior reviewer (NeurIPS/ICML level)
Scores the idea, identifies weaknesses, suggests minimum viable improvements
Provides concrete feedback on experimental design

Update IDEA_REPORT.md with reviewer feedback and revised plan.

Phase 5: Final Report

Finalize IDEA_REPORT.md with all accumulated information:

# Idea Discovery Report

**Direction**: $ARGUMENTS
**Date**: [today]
**Pipeline**: research-lit → idea-creator → novelty-check → research-review

## 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]

## Next Steps
- [ ] Implement Idea 1
- [ ] /run-experiment to deploy full-scale experiments
- [ ] /auto-review-loop to iterate until submission-ready
- [ ] Or invoke /research-pipeline for the complete end-to-end flow

Key Rules

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 ~/.claude/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)
implement                           ← write code for the top idea
/run-experiment                     ← deploy full-scale experiments
/auto-review-loop "top idea"        ← Workflow 2: iterate until submission-ready

Or use /research-pipeline for the full end-to-end flow.