`idea-discovery`¶

Pack: ARIS skills

Category: ideation

Field: —

License: MIT

Updated: 2026-05-18

Stages: rq-formulation · hypothesis-generation

↗ view SKILL.md on source · GitHub stars

Workflow 1: Idea Discovery Pipeline¶

Orchestrate a complete idea discovery workflow for: $ARGUMENTS

Overview¶

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

Text Only

/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.
REVIEWER_MODEL = gpt-5.5 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.5, o3, gpt-4o). Passed to sub-skills.
OUTPUT_DIR = idea-stage/ — All idea-stage outputs go here. Create the directory if it doesn't exist.
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.
COMPACT = false — When true, generate compact summary files for short-context models and session recovery. Writes idea-stage/IDEA_CANDIDATES.md (top 3-5 ideas only) at the end of this workflow. Downstream skills read this instead of the full idea-stage/IDEA_REPORT.md.
REF_PAPER = false — Reference paper to base ideas on. Accepts: local PDF path, arXiv URL, or any paper URL. When set, the paper is summarized first (idea-stage/REF_PAPER_SUMMARY.md), then idea generation uses it as context. Combine with base repo for "improve this paper with this codebase" workflows.

💡 These are defaults. Override by telling the skill, e.g., /idea-discovery "topic" — ref paper: https://arxiv.org/abs/2406.04329 or /idea-discovery "topic" — compact: true.

Pipeline¶

Phase 0: Load Research Brief (if available)¶

Before starting any other phase, check for a detailed research brief in the project:

Look for RESEARCH_BRIEF.md in the project root (or path passed as $ARGUMENTS)
If found, read it and extract:
Problem statement and context
Constraints (compute, data, timeline, venue)
What the user already tried / what didn't work
Domain knowledge and non-goals
Existing results (if any)
Use this as the primary context for all subsequent phases — it replaces the one-line prompt
If both RESEARCH_BRIEF.md and a one-line $ARGUMENTS exist, merge them (brief takes priority for details, argument sets the direction)

If no brief exists, proceed normally with $ARGUMENTS as the research direction.

💡 Create a brief from the template: cp templates/RESEARCH_BRIEF_TEMPLATE.md RESEARCH_BRIEF.md

Phase 0.5: Reference Paper Summary (when REF_PAPER is set)¶

Skip entirely if REF_PAPER is false.

Summarize the reference paper before searching the literature:

If arXiv URL (e.g., https://arxiv.org/abs/2406.04329):
Invoke /arxiv "ARXIV_ID" — download to fetch the PDF
Read the first 5 pages (title, abstract, intro, method overview)
If local PDF path (e.g., papers/reference.pdf):
Read the PDF directly (first 5 pages)
If other URL:
Fetch and extract content via WebFetch
Generate idea-stage/REF_PAPER_SUMMARY.md:

Markdown

## Reference Paper Summary

**Title**: [paper title]
**Authors**: [authors]
**Venue**: [venue, year]

### What They Did
[2-3 sentences: core method and contribution]

### Key Results
[Main quantitative findings]

### Limitations & Open Questions
[What the paper didn't solve, acknowledged weaknesses, future work suggestions]

### Potential Improvement Directions
[Based on the limitations, what could be improved or extended?]

### Codebase
[If `base repo` is also set: link to the repo and note which parts correspond to the paper]

🚦 Checkpoint: Present the summary to the user:

Text Only

📄 Reference paper summarized:
- Title: [title]
- Key limitation: [main gap]
- Improvement directions: [2-3 bullets]

Proceeding to literature survey with this as context.

Phase 1 and Phase 2 will use idea-stage/REF_PAPER_SUMMARY.md as additional context — /research-lit searches for related and competing work, /idea-creator generates ideas that build on or improve the reference paper.

Phase 1: Literature Survey¶

Invoke /research-lit to map the research landscape. Idea discovery is exactly the place where Gemini's AI-driven broad coverage adds value, so include gemini as a source by default unless the user already specified an explicit — sources: directive in their idea-discovery invocation:

Text Only

## If $ARGUMENTS already contains "— sources:", pass through unchanged
## (the user is in control of source selection):
/research-lit "$ARGUMENTS"

## Otherwise (the common case), include gemini explicitly for broader discovery:
/research-lit "$ARGUMENTS" — sources: all, gemini

If gemini-cli is not installed, /research-lit skips the Gemini source gracefully with a warning — no break to the pipeline. Users who want to force-disable Gemini in idea-discovery can pass /idea-discovery "topic" — sources: all explicitly (which becomes the literal source list, no auto-injection).

What this does: - Search arXiv, Google Scholar, Semantic Scholar for recent papers - Plus Gemini-driven broad discovery (sub-problem decomposition, naming variants, alias coverage) when gemini-cli is available - 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:

Text Only

📚 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 (and idea-stage/REF_PAPER_SUMMARY.md if available):

Text Only

/idea-creator "$ARGUMENTS"

What this does: - If idea-stage/REF_PAPER_SUMMARY.md exists, include it as context — ideas should build on, improve, or extend the reference paper - 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-stage/IDEA_REPORT.md

🚦 Checkpoint: Present idea-stage/IDEA_REPORT.md ranked ideas to the user. Ask:

Text Only

💡 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:

Text Only

/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-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:

Text Only

/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-stage/IDEA_REPORT.md with reviewer feedback and revised plan.

After review, refine the top idea into a concrete proposal and plan experiments:

Text Only

/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 GPT-5.4 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:

Text Only

🔬 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

Phase 5.5: Write Compact Files (when COMPACT = true)¶

Skip entirely if COMPACT is false.

Write idea-stage/IDEA_CANDIDATES.md — a lean summary of the top 3-5 surviving ideas:

Markdown

## Idea Candidates

| # | Idea | Pilot Signal | Novelty | Reviewer Score | Status |
|---|------|-------------|---------|---------------|--------|
| 1 | [title] | +X% | Confirmed | X/10 | RECOMMENDED |
| 2 | [title] | +Y% | Confirmed | X/10 | BACKUP |
| 3 | [title] | Negative | — | — | ELIMINATED |

### Active Idea: #1 — [title]
- Hypothesis: [one sentence]
- Key evidence: [pilot result]
- Next step: /experiment-bridge or /research-refine

This file is intentionally small (~30 lines) so downstream skills and session recovery can read it without loading the full idea-stage/IDEA_REPORT.md (~200+ lines).

Output Protocols¶

Follow these shared protocols for all output files: - Output Versioning Protocol — write timestamped file first, then copy to fixed name - Output Manifest Protocol — log every output to MANIFEST.md - Output Language Protocol — 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 ~/.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:

Text Only

/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.

idea-discovery¶

Workflow 1: Idea Discovery Pipeline¶

Overview¶

Constants¶

Pipeline¶

Phase 0: Load Research Brief (if available)¶

Phase 0.5: Reference Paper Summary (when REF_PAPER is set)¶

Phase 1: Literature Survey¶

Phase 2: Idea Generation + Filtering + Pilots¶

Phase 3: Deep Novelty Verification¶

Phase 4: External Critical Review¶

Phase 4.5: Method Refinement + Experiment Planning¶

Phase 5: Final Report¶

Phase 5.5: Write Compact Files (when COMPACT = true)¶

Output Protocols¶

Key Rules¶

Composing with Workflow 2¶

`idea-discovery`¶