paper-writing skill
Workflow 3: Full paper writing pipeline. Orchestrates paper-plan \u2192 paper-figure \u2192 paper-write \u2192 paper-compile \u2192 auto-paper-improvement-loop to go from a narrative report to a polished, submission-ready PDF. Use when user says \\\"\u5199\u8bba\u6587\u5168\u6d41\u7a0b\\\", \\\"write paper pipeline\\\", \\\"\u4ece\u62a5\u544a\u5230PDF\\\", \\\"paper writing\\\", or wants the complete paper generation workflow.
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Install the paper-writing skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep mkdir -p ~/.claude/skills cp -r /tmp/Auto-claude-code-research-in-sleep/skills/skills-codex-gemini-review/paper-writing ~/.claude/skills/paper-writing
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
Override for Codex users who want Gemini, not a second Codex agent, to act as the reviewer. Install this package after skills/skills-codex/*.
Workflow 3: Paper Writing Pipeline
Gemini overlay assurance: reviewindependence: cross-family and acceptancestatus: accepted.
Orchestrate a complete paper writing workflow for: $ARGUMENTS
Overview
This skill chains five sub-skills into a single automated pipeline:
/paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop
(outline) (plots) (LaTeX) (build PDF) (review & polish ×2)Each phase builds on the previous one's output. The final deliverable is a polished, reviewed paper/ directory with LaTeX source and compiled PDF.
Constants
- VENUE = ICLR — Target venue. Options: ICLR, NeurIPS, ICML. Affects style file, page limit, citation format.
- MAXIMPROVEMENTROUNDS = 2 — Number of review→fix→recompile rounds in the improvement loop.
- REVIEWERMODEL = gemini-review** — Gemini reviewer invoked through the local gemini-review MCP bridge for plan review, figure review, writing review, and the improvement loop.
- AUTOPROCEED = true** — Auto-continue between phases. Set false to pause and wait for user approval after each phase.
- HUMANCHECKPOINT = false** — When true, the improvement loop (Phase 5) pauses after each round's review to let you see the score and provide custom modification instructions. When false (default), the loop runs fully autonomously. Passed through to /auto-paper-improvement-loop.
Override inline: /paper-writing "NARRATIVE_REPORT.md" — venue: NeurIPS, human checkpoint: true
Inputs
This pipeline accepts one of:
- NARRATIVEREPORT.md** (best) — structured research narrative with claims, experiments, results, figures
- Research direction + experiment results — the skill will help draft the narrative first
- Existing PAPERPLAN.md** — skip Phase 1, start from Phase 2
The more detailed the input (especially figure descriptions and quantitative results), the better the output.
Pipeline
Phase 1: Paper Plan
Invoke /paper-plan to create the structural outline:
/paper-plan "$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
- Gemini reviews the plan for completeness via the /paper-plan overlay
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.tex for easy insertion
- Gemini reviews figure quality and captions via the /paper-figure overlay
Output: figures/ directory with PDFs, generation scripts, and LaTeX snippets.
Phase 2b: AI Illustration Generation (when illustration: true)
Skip this step entirely if illustration is not set or is false.
If the paper plan includes architecture diagrams, pipeline figures, or method illustrations, invoke /paper-illustration:
/paper-illustration "[method description from PAPER_PLAN.md or NARRATIVE_REPORT.md]"What this does:
- Codex plans the layout → Gemini optimizes → Nano Banana Pro renders → Codex reviews (score ≥ 9)
- Output: figures/aigenerated/.png — publication-quality method diagrams
- Requires GEMINIAPIKEY environment variable
Without illustration: true: Architecture diagrams must still be created manually (draw.io, Figma, TikZ) and placed in figures/ before proceeding — same as before.
Checkpoint: List generated vs manual figures.
📊 Figures complete:
- Data plots (auto): [list]
- AI illustrations (auto): [list, if illustration: true]
- 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?Writing invariant (every drafting and revision step): calibrate each
claim to its evidence and state it directly; generic caveats live in the
Limitations section only; writing instructions are never manuscript content
("do not mention X" means omit X, not "we do not address X"); tone edits
never change what the paper knows; the paper is a launch, not a progress
report — organize around the strongest advantage, give every experiment an
argumentative duty, keep unfavorable numbers in the tables and explain them
as tradeoffs only where the evidence supports that, never narrating
defeats. /paper-write carries the full CONFIDENT PROSE, HONEST LIMITS
contract.
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.bib from citation scaffolding
- Clean stale files from previous section structures
- Automated bib cleaning (remove uncited entries)
- De-AI polish (remove "delve", "pivotal", "landscape"...)
- Gemini reviews each section for quality via the /paper-write overlay
Output: paper/ directory with main.tex, sections/.tex, references.bib, mathcommands.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:
More skills from wanshuiyin/Auto-claude-code-research-in-sleep
- Aablation-plannerUse when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
- Aablation-plannerUse when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.
- AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
- AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
- Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
- Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says \"analyze results\", \"compare\", or needs to interpret experimental data.
- AarxivSearch, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv", "arxiv search", "get paper pdf", or wants to find and save papers from arXiv to the local paper library.
- AarxivSearch, download, and summarize academic papers from arXiv. Use when user says \"search arxiv\", \"download paper\", \"fetch arxiv\", \"arxiv search\", \"get paper pdf\", or wants to find and save papers from arXiv to the local paper library.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via Gemini review through gemini-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.