/aris-paper-writing

name: aris-paper-writing
description: "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."
argument-hint: "[narrative-report-path-or-topic]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

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:

/aris-paper-plan → /aris-paper-figure → /aris-paper-write → /aris-paper-compile → /aris-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.

In this hybrid pack, the pipeline itself is unchanged, but `aris-paper-plan` and `aris-paper-write` use Orchestra-adapted shared references for stronger story framing and prose guidance.

Constants

• **VENUE = `ICLR`** — Target venue. Options: `ICLR`, `NeurIPS`, `ICML`, `CVPR`, `ACL`, `AAAI`, `ACM`, `IEEE_JOURNAL` (IEEE Transactions / Letters), `IEEE_CONF` (IEEE conferences). 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 `false` to 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. When `false` (default), the loop runs fully autonomously. Passed through to `/aris-auto-paper-improvement-loop`.

> Override inline: `/aris-paper-writing "NARRATIVE_REPORT.md" — venue: NeurIPS, human checkpoint: true` > IEEE example: `/aris-paper-writing "NARRATIVE_REPORT.md" — venue: IEEE_JOURNAL`

Inputs

This pipeline accepts one of:

1. **`NARRATIVE_REPORT.md`** (best) — structured research narrative with claims, experiments, results, figures 2. **Research direction + experiment results** — the skill will help draft the narrative first 3. **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 `/aris-paper-plan` to create the structural outline:

/aris-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 `/aris-paper-figure` to generate data-driven plots and tables:

/aris-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
• 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 `/aris-paper-figure` SKILL.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 `/aris-paper-write` to generate section-by-section LaTeX:

/aris-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"...)
• 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 `/aris-paper-compile` to build the PDF:

/aris-paper-compile "paper/"

**What this does:**

• `latexmk -pdf` with automatic multi-pass compilation
• Auto-