> paper-writing
Workflow 3: Full paper writing pipeline. Orchestrates paper-plan → paper-figure → paper-write → paper-compile → auto-paper-improvement-loop to go from a narrative report to a polished, submission-ready PDF. Use when user says "写论文全流程", "write paper pipeline", "从报告到PDF", "paper writing", or wants the complete paper generation workflow.
curl "https://skillshub.wtf/wanshuiyin/Auto-claude-code-research-in-sleep/paper-writing?format=md"Workflow 3: Paper Writing Pipeline
Orchestrate a complete paper writing workflow for: $ARGUMENTS
Overview
This skill chains five sub-skills into a single automated pipeline:
/paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop
(outline) (plots) (LaTeX) (build PDF) (review & polish ×2)
Each phase builds on the previous one's output. The final deliverable is a polished, reviewed paper/ directory with LaTeX source and compiled PDF.
Constants
- VENUE =
ICLR— Target venue. Options:ICLR,NeurIPS,ICML. Affects style file, page limit, citation format. - MAX_IMPROVEMENT_ROUNDS = 2 — Number of review→fix→recompile rounds in the improvement loop.
- REVIEWER_MODEL =
gpt-5.4— Model used via Codex MCP for plan review, figure review, writing review, and improvement loop. - AUTO_PROCEED = true — Auto-continue between phases. Set
falseto pause and wait for user approval after each phase. - HUMAN_CHECKPOINT = false — When
true, the improvement loop (Phase 5) pauses after each round's review to let you see the score and provide custom modification instructions. Whenfalse(default), the loop runs fully autonomously. Passed through to/auto-paper-improvement-loop.
Override inline:
/paper-writing "NARRATIVE_REPORT.md" — venue: NeurIPS, human checkpoint: true
Inputs
This pipeline accepts one of:
NARRATIVE_REPORT.md(best) — structured research narrative with claims, experiments, results, figures- Research direction + experiment results — the skill will help draft the narrative first
- Existing
PAPER_PLAN.md— skip Phase 1, start from Phase 2
The more detailed the input (especially figure descriptions and quantitative results), the better the output.
Pipeline
Phase 1: Paper Plan
Invoke /paper-plan to create the structural outline:
/paper-plan "$ARGUMENTS"
What this does:
- Parse NARRATIVE_REPORT.md for claims, evidence, and figure descriptions
- Build a Claims-Evidence Matrix — every claim maps to evidence, every experiment supports a claim
- Design section structure (5-8 sections depending on paper type)
- Plan figure/table placement with data sources
- Scaffold citation structure
- GPT-5.4 reviews the plan for completeness
Output: PAPER_PLAN.md with section plan, figure plan, citation scaffolding.
Checkpoint: Present the plan summary to the user.
📐 Paper plan complete:
- Title: [proposed title]
- Sections: [N] ([list])
- Figures: [N] auto-generated + [M] manual
- Target: [VENUE], [PAGE_LIMIT] pages
Shall I proceed with figure generation?
- User approves (or AUTO_PROCEED=true) → proceed to Phase 2.
- User requests changes → adjust plan and re-present.
Phase 2: Figure Generation
Invoke /paper-figure to generate data-driven plots and tables:
/paper-figure "PAPER_PLAN.md"
What this does:
- Read figure plan from PAPER_PLAN.md
- Generate matplotlib/seaborn plots from JSON/CSV data
- Generate LaTeX comparison tables
- Create
figures/latex_includes.texfor easy insertion - GPT-5.4 reviews figure quality and captions
Output: figures/ directory with PDFs, generation scripts, and LaTeX snippets.
Scope: Auto-generates ~60% of figures (data plots, comparison tables). Architecture diagrams, pipeline figures, and qualitative result grids must be created manually and placed in
figures/before proceeding. See/paper-figureSKILL.md for details.
Checkpoint: List generated vs manual figures.
📊 Figures complete:
- Auto-generated: [list]
- Manual (need your input): [list]
- LaTeX snippets: figures/latex_includes.tex
[If manual figures needed]: Please add them to figures/ before I proceed.
[If all auto]: Shall I proceed with LaTeX writing?
Phase 3: LaTeX Writing
Invoke /paper-write to generate section-by-section LaTeX:
/paper-write "PAPER_PLAN.md"
What this does:
- Write each section following the plan, with proper LaTeX formatting
- Insert figure/table references from
figures/latex_includes.tex - Build
references.bibfrom citation scaffolding - Clean stale files from previous section structures
- Automated bib cleaning (remove uncited entries)
- De-AI polish (remove "delve", "pivotal", "landscape"...)
- GPT-5.4 reviews each section for quality
Output: paper/ directory with main.tex, sections/*.tex, references.bib, math_commands.tex.
Checkpoint: Report section completion.
✍️ LaTeX writing complete:
- Sections: [N] written ([list])
- Citations: [N] unique keys in references.bib
- Stale files cleaned: [list, if any]
Shall I proceed with compilation?
Phase 4: Compilation
Invoke /paper-compile to build the PDF:
/paper-compile "paper/"
What this does:
latexmk -pdfwith automatic multi-pass compilation- Auto-fix common errors (missing packages, undefined refs, BibTeX syntax)
- Up to 3 compilation attempts
- Post-compilation checks: undefined refs, page count, font embedding
- Precise page verification via
pdftotext - Stale file detection
Output: paper/main.pdf
Checkpoint: Report compilation results.
🔨 Compilation complete:
- Status: SUCCESS
- Pages: [X] (main body) + [Y] (references) + [Z] (appendix)
- Within page limit: YES/NO
- Undefined references: 0
- Undefined citations: 0
Shall I proceed with the improvement loop?
Phase 5: Auto Improvement Loop
Invoke /auto-paper-improvement-loop to polish the paper:
/auto-paper-improvement-loop "paper/"
What this does (2 rounds):
Round 1: GPT-5.4 xhigh reviews the full paper → identifies CRITICAL/MAJOR/MINOR issues → Claude Code implements fixes → recompile → save main_round1.pdf
Round 2: GPT-5.4 xhigh re-reviews with conversation context → identifies remaining issues → Claude Code implements fixes → recompile → save main_round2.pdf
Typical improvements:
- Fix assumption-model mismatches
- Soften overclaims to match evidence
- Add missing interpretations and notation
- Strengthen limitations section
- Add theory-aligned experiments if needed
Output: Three PDFs for comparison + PAPER_IMPROVEMENT_LOG.md.
Format check (included in improvement loop Step 8): After final recompilation, auto-detect and fix overfull hboxes (content exceeding margins), verify page count vs venue limit, and ensure compact formatting. Any overfull > 10pt is fixed before generating the final PDF.
Phase 6: Final Report
# Paper Writing Pipeline Report
**Input**: [NARRATIVE_REPORT.md or topic]
**Venue**: [ICLR/NeurIPS/ICML]
**Date**: [today]
## Pipeline Summary
| Phase | Status | Output |
|-------|--------|--------|
| 1. Paper Plan | ✅ | PAPER_PLAN.md |
| 2. Figures | ✅ | figures/ ([N] auto + [M] manual) |
| 3. LaTeX Writing | ✅ | paper/sections/*.tex ([N] sections, [M] citations) |
| 4. Compilation | ✅ | paper/main.pdf ([X] pages) |
| 5. Improvement | ✅ | [score0]/10 → [score2]/10 |
## Improvement Scores
| Round | Score | Key Changes |
|-------|-------|-------------|
| Round 0 | X/10 | Baseline |
| Round 1 | Y/10 | [summary] |
| Round 2 | Z/10 | [summary] |
## Deliverables
- paper/main.pdf — Final polished paper
- paper/main_round0_original.pdf — Before improvement
- paper/main_round1.pdf — After round 1
- paper/main_round2.pdf — After round 2
- paper/PAPER_IMPROVEMENT_LOG.md — Full review log
## Remaining Issues (if any)
- [items from final review that weren't addressed]
## Next Steps
- [ ] Visual inspection of PDF
- [ ] Add any missing manual figures
- [ ] Submit to [venue] via OpenReview / CMT / HotCRP
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. -
Don't skip phases. Each phase builds on the previous one — skipping leads to errors.
-
Checkpoint between phases when AUTO_PROCEED=false. Present results and wait for approval.
-
Manual figures first. If the paper needs architecture diagrams or qualitative results, the user must provide them before Phase 3.
-
Compilation must succeed before entering the improvement loop. Fix all errors first.
-
Preserve all PDFs. The user needs round0/round1/round2 for comparison.
-
Document everything. The pipeline report should be self-contained.
-
Respect page limits. If the paper exceeds the venue limit, suggest specific cuts before the improvement loop.
Composing with Other Workflows
/idea-discovery "direction" ← Workflow 1: find ideas
implement ← write code
/run-experiment ← deploy experiments
/auto-review-loop "paper topic" ← Workflow 2: iterate research
/paper-writing "NARRATIVE_REPORT.md" ← Workflow 3: you are here
submit! 🎉
Or use /research-pipeline for the Workflow 1+2 end-to-end flow,
then /paper-writing for the final writing step.
Typical Timeline
| Phase | Duration | Can sleep? |
|---|---|---|
| 1. Paper Plan | 5-10 min | No |
| 2. Figures | 5-15 min | No |
| 3. LaTeX Writing | 15-30 min | Yes ✅ |
| 4. Compilation | 2-5 min | No |
| 5. Improvement | 15-30 min | Yes ✅ |
Total: ~45-90 min for a full paper from narrative report to polished PDF.
> related_skills --same-repo
> run-experiment
Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
> research-review
Get a deep critical review of research from GPT via Codex MCP. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
> research-refine
Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.4 review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.
> research-refine-pipeline
Run an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.