research-pipeline

name	research-pipeline
description	Full research pipeline: Workflow 1 (idea discovery) → implementation → Workflow 2 (auto review loop) → Workflow 3 (paper writing, optional). Goes from a broad research direction all the way to a polished PDF. 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

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, /research-lit downloads the top relevant arXiv PDFs during literature survey. When false (default), only fetches metadata via arXiv API. Passed through to /idea-discovery → /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 /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 /auto-review-loop.
• AUTO_WRITE = false — When true, automatically invoke Workflow 3 (/paper-writing) after Stage 5. Requires VENUE to be set. When false (default), Stage 5 generates NARRATIVE_REPORT.md and stops — user invokes /paper-writing manually.
• VENUE = ICLR — Target venue for paper writing (Stage 6). Only used when AUTO_WRITE=true. Options: ICLR, NeurIPS, ICML, CVPR, ACL, AAAI, ACM, IEEE_CONF, IEEE_JOURNAL.

💡 Override via argument, e.g., /research-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare, auto_write: true, venue: NeurIPS.

Overview

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

/idea-discovery → implement → /run-experiment → /auto-review-loop → /paper-writing (optional)
├── Workflow 1 ──┤            ├────────── Workflow 2 ──────────────┤ ├── Workflow 3 ──┤

It orchestrates up to three major workflows plus the implementation bridge between them. Workflow 3 (paper writing) is optional and controlled by AUTO_WRITE.

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 templates/RESEARCH_BRIEF_TEMPLATE.md.

Invoke the idea discovery pipeline:

/idea-discovery "$ARGUMENTS"

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

Output: idea-stage/IDEA_REPORT.md with ranked, validated, pilot-tested ideas.

🚦 Gate 1 — Human Checkpoint:

After idea-stage/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 /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-stage/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-stage/IDEA_REPORT.md (hypothesis, experimental design, pilot code) (fall back to ./IDEA_REPORT.md if not found)

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. Route by job count:

Small batch (≤5 jobs) — direct deployment:

/run-experiment [experiment command]

Large batch (≥10 jobs, multi-seed sweeps, teacher→student chains) — use the queue scheduler:

/experiment-queue [grid spec or manifest]

experiment-bridge (Workflow 1.5) auto-routes based on milestone job count. For pipeline runs with multi-seed sweeps from the start, you can override globally with --- batch: queue to force /experiment-queue for all milestones.

What this does:

• Check GPU availability on configured servers
• Sync code to remote server
• Launch experiments in screen sessions with proper CUDA_VISIBLE_DEVICES
• For /experiment-queue: also OOM retry, stale-screen cleanup, phase dependencies, crash-safe state
• Verify experiments started successfully

Monitor progress:

/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:

/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 Code implements fixes (code changes, new experiments, reframing)
3. Deploy fixes, collect new results
4. Re-review → repeat until score ≥ 6/10 or 4 rounds reached

Output: review-stage/AUTO_REVIEW.md with full review history and final assessment.

Stage 5: Research Summary & Writing Handoff

After the auto-review loop completes, prepare the handoff for paper writing.

Step 1: Write a final research status report (same as before).

Step 2: Generate NARRATIVE_REPORT.md from:

• IDEA_REPORT.md (chosen idea, hypothesis, novelty justification)
• Implementation details from the repo
• Experiment configs and final results
• AUTO_REVIEW.md (review history, weaknesses fixed, remaining limitations)

The narrative report must contain:

• Problem statement and core claim
• Method summary
• Key quantitative results with evidence for each claim
• Figure/table inventory (which exist, which need manual creation)
• Limitations and remaining follow-up items

Output: NARRATIVE_REPORT.md + research pipeline report.

# Research Pipeline Report

**Direction**: $ARGUMENTS
**Chosen Idea**: [title]
**Date**: [start] → [end]
**Pipeline**: idea-discovery → implement → run-experiment → auto-review-loop

## Journey Summary
- Ideas generated: X → filtered to Y → piloted Z → chose 1
- Implementation: [brief description of what was built]
- Experiments: [number of GPU experiments, total compute time]
- Review rounds: N/4, final score: X/10

## Writing Handoff
- NARRATIVE_REPORT.md: ✅ generated
- Venue: [VENUE or "not set — run /paper-writing manually"]
- Manual figures needed: [list or "none"]

## Remaining TODOs (if any)
- [items flagged by reviewer that weren't addressed]

Stage 6: Paper Writing (Workflow 3 — Optional)

Skip this stage if AUTO_WRITE=false (default). Present the /paper-writing command for manual use:

📝 Research complete. To write the paper:
/paper-writing "NARRATIVE_REPORT.md" — venue: ICLR

If AUTO_WRITE=true:

🚦 Gate 2 — Writing Checkpoint:

📝 Research pipeline complete. Ready for Workflow 3.

- Venue: [VENUE]
- Input: NARRATIVE_REPORT.md
- Manual figures required: [list or none]
- Next step: /paper-writing "NARRATIVE_REPORT.md — venue: [VENUE]"

Proceeding with paper writing...

Checks before proceeding:

• If VENUE is missing → stop and ask. Do NOT silently use a default venue.
• If manual figures are required → pause and list them. Wait for user to add them.

Then invoke:

/paper-writing "NARRATIVE_REPORT.md" — venue: $VENUE

This delegates to Workflow 3 which handles its own phases: /paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop

When Workflow 3 finishes, update the pipeline report with:

• Paper writing completion status
• Final PDF path (paper/main.pdf)
• Improvement scores (round 0 → round N)
• Remaining issues

Output: paper/ directory with LaTeX source, compiled PDF, and PAPER_IMPROVEMENT_LOG.md.

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.

• Human checkpoint after Stage 1 is controlled by AUTO_PROCEED. When false, do not proceed without user confirmation. When true, auto-select the top idea after presenting results.

• Stages 2-4 can run autonomously once the user confirms the idea. This is the "sleep and wake up to results" part.

• If Stage 4 ends at round 4 without positive assessment, stop and report remaining issues. Do not loop forever.

• Budget awareness: Track total GPU-hours across the pipeline. Flag if approaching user-defined limits.

• Documentation: Every stage updates its own output file. The full history should be self-contained.

• Fail gracefully: If any stage fails (no good ideas, experiments crash, review loop stuck), report clearly and suggest alternatives rather than forcing forward.

Typical Timeline

Stage	Duration	Can sleep?
1. Idea Discovery	30-60 min	Yes if AUTO_PROCEED=true
2. Implementation	15-60 min	Yes (autonomous after Gate 1)
3. Deploy	5 min + experiment time	Yes ✅
4. Auto Review	1-4 hours (depends on experiments)	Yes ✅

Sweet spot: Run Stage 1-2 in the evening, launch Stage 3-4 before bed, wake up to a reviewed paper.