/aris-idea-discovery

name: aris-idea-discovery
description: "Workflow 1: Full idea discovery pipeline. Orchestrates research-lit → idea-creator → novelty-check → research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says \"找idea全流程\", \"idea discovery pipeline\", \"从零开始找方向\", or wants the complete idea exploration workflow."
argument-hint: "[research-direction]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

Workflow 1: Idea Discovery Pipeline

Orchestrate a complete idea discovery workflow for: **$ARGUMENTS**

Overview

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

/aris-research-lit → /aris-idea-creator → /aris-novelty-check → /aris-research-review → /aris-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_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-5.4`** — 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`, `/aris-research-lit` downloads the top relevant arXiv PDFs during Phase 1. When `false` (default), only fetches metadata. Passed through to `/aris-research-lit`.
• **COMPACT = false** — When `true`, generate compact summary files for short-context models and session recovery. Writes `IDEA_CANDIDATES.md` (top 3-5 ideas only) at the end of this workflow. Downstream skills read this instead of the full `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 (`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., `/aris-idea-discovery "topic" — ref paper: https://arxiv.org/abs/2406.04329` or `/aris-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:

1. Look for `RESEARCH_BRIEF.md` in the project root (or path passed as `$ARGUMENTS`) 2. 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)

3. Use this as the primary context for all subsequent phases — it replaces the one-line prompt 4. 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: copy [`RESEARCH_BRIEF_TEMPLATE.md`](../aris-infra/templates/RESEARCH_BRIEF_TEMPLATE.md) (in the `aris-infra` folder beside this skill) to `RESEARCH_BRIEF.md` in the project root.

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:

1. **If arXiv URL** (e.g., `https://arxiv.org/abs/2406.04329`):

• Invoke `/aris-arxiv "ARXIV_ID" — download` to fetch the PDF
• Read the first 5 pages (title, abstract, intro, method overview)

2. **If local PDF path** (e.g., `papers/reference.pdf`):

• Read the PDF directly (first 5 pages)

3. **If other URL**:

• Fetch and extract content via WebFetch

4. **Generate `REF_PAPER_SUMMARY.md`**:

# 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:

📄 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 `REF_PAPER_SUMMARY.md` as additional context — `/aris-research-lit` searches for related and competing work, `/aris-idea-creator` generates ideas that build on or improve the reference paper.

Phase 1: Literature Survey

Invoke `/aris-research-lit` to map the research landscape:

/aris-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