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 — Passed through to /auto-review-loop. medium uses Codex xhigh review; hard adds Reviewer Memory and Debate Protocol; nightmare adds direct repository-reading adversarial verification.
• 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 so the user can invoke /paper-writing manually.
• VENUE = ICLR — Target venue for paper writing 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 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 loaded by /idea-discovery as detailed context and used as the primary brief for the pipeline. The one-line $ARGUMENTS still sets the high-level direction.

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)

1. 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

1. 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) — queue scheduler:

/experiment-queue [grid spec or manifest]

experiment-bridge auto-routes based on milestone job count. For pipeline runs with multi-seed sweeps from the start, allow an explicit batch: queue override 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, and 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]"

Pass REVIEWER_DIFFICULTY through unchanged. For hard and nightmare, the downstream loop must preserve Reviewer Memory, Debate Protocol, Review Tracing, and any saved reviewer agent_id across rounds.

What this does (up to 4 rounds):

1. GPT-5.4 xhigh reviews the work (score, weaknesses, minimum fixes)
2. Codex 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 the final research status report.

Step 2: Generate NARRATIVE_REPORT.md from:

• idea-stage/IDEA_REPORT.md (chosen idea, hypothesis, novelty justification)
• implementation details from the repo
• experiment configs and final results
• review-stage/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 manual command:

/paper-writing "NARRATIVE_REPORT.md" — venue: ICLR

If AUTO_WRITE=true, stop and ask if VENUE is missing. Do not silently use a default venue. If manual figures are required, pause and list them before invoking paper writing.

When ready, invoke:

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

Workflow 3 handles its own phases: /paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop. When it finishes, update the pipeline report with final PDF path, improvement scores, and remaining issues.

Output Protocols

Follow these shared protocols for all output files: - [Output Versioning Protocol](../../shared-references/output-versioning.md) — write timestamped file first, then copy to fixed name - [Output Manifest Protocol](../../shared-references/output-manifest.md) — log every output to MANIFEST.md - [Output Language Protocol](../../shared-references/output-language.md) — 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

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