Research Pipeline

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 3) 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.
• CODE_REVIEW = true — GPT-5.4 xhigh reviews experiment code before deployment. Catches logic bugs before wasting GPU hours. Set false to skip. Passed through to /experiment-bridge.
• BASE_REPO = false — GitHub repo URL to use as base codebase. When set, /experiment-bridge clones the repo first and implements experiments on top of it. When false (default), writes code from scratch or reuses existing project files. Passed through to /experiment-bridge.
• COMPACT = false — When true, generates compact summary files for short-context models and session recovery. Passed through to /idea-discovery and /experiment-bridge.
• AUTO_WRITE = false — When true, automatically invoke Workflow 3 (/paper-writing) after Stage 4. Requires VENUE to be set. When false (default), Stage 4 generates NARRATIVE_REPORT.md and stops — user invokes /paper-writing manually.
• VENUE = ICLR — Target venue for paper writing (Stage 5). Only used when AUTO_WRITE=true. Options: ICLR, NeurIPS, ICML, CVPR, ACL, AAAI, ACM, IEEE_CONF, IEEE_JOURNAL.
• RENDER_HTML = true — When true (default), auto-render NARRATIVE_REPORT.md to HTML at Stage 4 completion via /render-html. Uses --no-review (this is an internal handoff doc to /paper-writing, not a reviewer-facing final artifact — the upstream Stage 3 auto-review loop already cross-model-reviewed the claims). Set false to skip, or pass — render html: false. Non-blocking: if /render-html fails or Codex MCP is unavailable, log the failure and continue — the HTML view is a nice-to-have, not a Stage 4 prerequisite.

💡 Override via argument, e.g., /research-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare, code review: false, base repo: https://github.com/org/project, auto_write: true, venue: NeurIPS.

Overview

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

/idea-discovery → /experiment-bridge → /auto-review-loop → /paper-writing (optional)
├── Workflow 1 ──┤├── Workflow 1.5 ──┤├── Workflow 2 ───┤ ├── Workflow 3 ──┤

It orchestrates up to four major workflows in sequence. 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.

Review Tracing follows the downstream review skills. Stage 1 and Stage 3 preserve reviewer prompts/responses through their own trace protocols so the final handoff can be audited.

🚦 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 the idea → proceed to Stage 2. /experiment-bridge reads refine-logs/EXPERIMENT_PLAN.md already generated by /idea-discovery.
• 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-proceeds after presenting results. The rest of the pipeline (Stages 2-3) is expensive (GPU time + multiple review rounds), so set AUTO_PROCEED=false if you want a final review checkpoint before committing GPU resources.

Stage 2: Experiment Bridge (Workflow 1.5)

Once the user confirms which idea to pursue, delegate implementation and deployment to /experiment-bridge:

/experiment-bridge "$CHOSEN_IDEA_TITLE" — code review: $CODE_REVIEW, base repo: $BASE_REPO, compact: $COMPACT

💡 Queue routing is automatic: /experiment-bridge Phase 4 routes each milestone by job count — ≤5 jobs → /run-experiment, ≥10 jobs or teacher→student phase dependencies → /experiment-queue (with OOM retry, wave gating, crash-safe state). No manual override is needed.

What this does (fully autonomous):

1. Parses refine-logs/EXPERIMENT_PLAN.md — extracts milestones, run order, compute budget
2. Implements experiment code — extends pilot to full scale, follows existing codebase conventions
3. Cross-model code review — GPT-5.4 xhigh reviews the implementation for logic bugs, incorrect metrics, and ground-truth misuse before any GPU time is spent
4. Sanity check — runs the smallest experiment first to verify the environment; auto-debugs failures (up to 3 attempts, with /codex:rescue fallback)
5. Deploys full experiments — auto-routes by job count (≤5 → /run-experiment, ≥10 → /experiment-queue with OOM retry, wave gating, crash-safe state)
6. Collects initial results — parses outputs, updates refine-logs/EXPERIMENT_TRACKER.md, runs /training-check if W&B is configured
7. Auto-plans ablations via /ablation-planner if main results are positive

Output:

• refine-logs/EXPERIMENT_RESULTS.md — structured results by milestone
• refine-logs/EXPERIMENT_TRACKER.md — updated run-by-run status
• EXPERIMENT_LOG.md (when COMPACT=true) — session-recovery-friendly log

Monitor progress (while experiments run):

/monitor-experiment [server]

Wait for /experiment-bridge to complete and report its handoff summary before proceeding.

Stage 3: Auto Review Loop (Workflow 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 4: 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 → experiment-bridge → 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 5 / Stage 6: Paper Writing (Workflow 3 — Optional)

This is the Stage 6: Paper Writing handoff in the broader research lifecycle; it is numbered Stage 5 here because this consolidated pipeline counts the writing handoff after the Stage 4 narrative report.

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.

Render HTML view (auto, when RENDER_HTML = true)

After Stage 4 finalizes NARRATIVE_REPORT.md (before paper writing branches), invoke /render-html on the narrative report:

/render-html "NARRATIVE_REPORT.md" --no-review

--no-review is intentional: this is an internal handoff doc, not reviewer-facing — the claims it summarizes were already cross-model-reviewed in Stage 3's /auto-review-loop. Output: NARRATIVE_REPORT.html next to the MD, with embedded source SHA256.

Non-blocking: if /render-html fails (helper missing, file write error, etc.), log the failure and continue Stage 4 — the HTML view is a convenience artifact, not a pipeline prerequisite.

Skip this step if RENDER_HTML = false.

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 2-3 can run autonomously once the user confirms the idea. This is the "sleep and wake up to results" part.

• If Stage 3 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 in the evening, launch Stage 2-3 before bed, wake up to a reviewed paper.