/aris-research-pipeline

name: aris-research-pipeline
description: "Full research pipeline: Workflow 1 (idea discovery) → implementation → Workflow 2 (auto review loop). Goes from a broad research direction all the way to a submission-ready paper. Use when user says \"全流程\", \"full pipeline\", \"从找idea到投稿\", \"end-to-end research\", or wants the complete autonomous research lifecycle."
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"

Full Research Pipeline: Idea → Experiments → Submission

End-to-end autonomous research workflow for: **$ARGUMENTS**

Constants

• **AUTO_PROCEED = true** — When `true`, Gate 1 auto-selects the top-ranked idea (highest pilot signal + novelty confirmed) and continues to implementation. When `false`, always waits for explicit user confirmation before proceeding.
• **ARXIV_DOWNLOAD = false** — When `true`, `/aris-research-lit` downloads the top relevant arXiv PDFs during literature survey. When `false` (default), only fetches metadata via arXiv API. Passed through to `/aris-idea-discovery` → `/aris-research-lit`.
• **HUMAN_CHECKPOINT = false** — When `true`, the auto-review loops (Stage 4) pause after each round's review to let you see the score and provide custom modification instructions before fixes are implemented. When `false` (default), loops run fully autonomously. Passed through to `/aris-auto-review-loop`.
• **REVIEWER_DIFFICULTY = medium** — How adversarial the reviewer is. `medium` (default): standard MCP review. `hard`: adds reviewer memory + debate protocol. `nightmare`: GPT reads repo directly via `codex exec` + memory + debate. Passed through to `/aris-auto-review-loop`.

> 💡 Override via argument, e.g., `/aris-research-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare`.

Overview

This skill chains the entire research lifecycle into a single pipeline:

/aris-idea-discovery → implement → /aris-run-experiment → /aris-auto-review-loop → submission-ready
├── Workflow 1 ──┤            ├────────── Workflow 2 ──────────────┤

It orchestrates two major workflows plus the implementation bridge between them.

Pipeline

Stage 1: Idea Discovery (Workflow 1)

If `RESEARCH_BRIEF.md` exists in the project root, it will be automatically loaded as detailed context (replaces one-line prompt). See [`RESEARCH_BRIEF_TEMPLATE.md`](../aris-infra/templates/RESEARCH_BRIEF_TEMPLATE.md) in the `aris-infra` folder beside this skill.

Invoke the idea discovery pipeline:

/aris-idea-discovery "$ARGUMENTS"

This internally runs: `/aris-research-lit` → `/aris-idea-creator` → `/aris-novelty-check` → `/aris-research-review`

**Output:** `IDEA_REPORT.md` with ranked, validated, pilot-tested ideas.

**🚦 Gate 1 — Human Checkpoint:**

After `IDEA_REPORT.md` is generated, **pause and present the top ideas to the user**:

📋 Idea Discovery complete. Top ideas:

1. [Idea 1 title] — Pilot: POSITIVE (+X%), Novelty: CONFIRMED
2. [Idea 2 title] — Pilot: WEAK POSITIVE (+Y%), Novelty: CONFIRMED
3. [Idea 3 title] — Pilot: NEGATIVE, eliminated

Recommended: Idea 1. Shall I proceed with implementation?

**If AUTO_PROCEED=false:** Wait for user confirmation before continuing. The user may:

• **Approve an idea** → proceed to Stage 2.
• **Pick a different idea** → proceed with their choice.
• **Request changes** (e.g., "combine Idea 1 and 3", "focus more on X") → update the idea prompt with user feedback, re-run `/aris-idea-discovery` with refined constraints, and present again.
• **Reject all ideas** → collect feedback on what's missing, re-run Stage 1 with adjusted research direction. Repeat until the user commits to an idea.
• **Stop here** → save current state to `IDEA_REPORT.md` for future reference.

**If AUTO_PROCEED=true:** Present the top ideas, wait 10 seconds for user input. If no response, auto-select the #1 ranked idea (highest pilot signal + novelty confirmed) and proceed to Stage 2. Log: `"AUTO_PROCEED: selected Idea 1 — [title]"`.

> ⚠️ **This gate waits for user confirmation when AUTO_PROCEED=false.** When `true`, it auto-selects the top idea after presenting results. The rest of the pipeline (Stages 2-4) is expensive (GPU time + multiple review rounds), so set `AUTO_PROCEED=false` if you want to manually choose which idea to pursue.

Stage 2: Implementation

Once the user confirms which idea to pursue:

1. **Read the idea details** from `IDEA_REPORT.md` (hypothesis, experimental design, pilot code)

2. **Implement the full experiment**:

• Extend pilot code to full scale (multi-seed, full dataset, proper baselines)
• Add proper evaluation metrics and logging (wandb if configured)
• Write clean, reproducible experiment scripts
• Follow existing codebase conventions

3. **Code review**: Before deploying, do a self-review:

• Are all hyperparameters configurable via argparse?
• Is the random seed fixed and controllable?
• Are results saved to JSON/CSV for later analysis?
• Is there proper logging for debugging?

Stage 3: Deploy Experiments (Workflow 2 — Part 1)

Deploy the full-scale experiments:

/aris-run-experiment [experiment command]

**What this does:**

• Check GPU availability on configured servers
• Sync code to remote server
• Launch experiments in screen sessions with proper CUDA_VISIBLE_DEVICES
• Verify experiments started successfully

**Monitor progress:**

/aris-monitor-experiment [server]

Wait for experiments to complete. Collect results.

Stage 4: Auto Review Loop (Workflow 2 — Part 2)

Once initial results are in, start the autonomous improvement loop:

/aris-auto-review-loop "$ARGUMENTS — [chosen idea title], difficulty: $REVIEWER_DIFFICULTY"

**What this does (up to 4 rounds):** 1. GPT-5.4 xhigh reviews the work (score, weaknesses, minimum fixes) 2. Claude Cod