Agent skill

Aris Paper Writing

by OpenLAIR in OpenLAIR/dr-claw

Workflow 3: Full paper writing pipeline. An agent skill from OpenLAIR/dr-claw.

MITAuto-check: warningsResearch & Science

Install Aris Paper Writing

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add OpenLAIR/dr-claw --skill aris-paper-writing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install OpenLAIR/dr-claw aris-paper-writing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/aris-paper-writing .claude/skills/aris-paper-writing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
aris-paper-writing
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.6k tokens
SKILL.md length
856 words
Files
1
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

Workflow 3: Full paper writing pipeline. An agent skill from OpenLAIR/dr-claw.

  • Works in 6 steps: Paper Plan → Figure Generation → LaTeX Writing → …
  • User says 写论文全流程
  • SKILL.md covers Overview, Constants, Inputs and Pipeline, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Aris Paper Writing is an agent skill from OpenLAIR/dr-claw. 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.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Scientific writing. The repository describes itself as: A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power. The licence is MIT.

When your agent uses it

  • User says 写论文全流程
  • Write paper pipeline
  • Wants the complete paper generation workflow

Example prompts

  • “写论文全流程”
  • “write paper pipeline”
  • “从报告到PDF”
  • “/aris-paper-writing”

Requirements

  • Pre-approved tools (allowed-tools): Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Paper Plan
  2. Figure Generation
  3. LaTeX Writing
  4. Compilation
  5. Auto Improvement Loop
  6. Final Report

What it can do on your machine

Read from SKILL.md and the folder at commit d51b64e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(*)
    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Agent
    • Skill
    • mcp__codex__codex
    • mcp__codex__codex-reply

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Aris Paper Writing loads about 2.6k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 856 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

  • WarningTells the agent its actions are pre-authorized / not to stop for confirmationSKILL.md:246
    t << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently.
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from OpenLAIR/dr-claw at commit d51b64e, republished under its MIT licence (© OpenLAIR). 856 words, ~2,650 tokens.

Download SKILL.mdSave it as .claude/skills/aris-paper-writing/SKILL.md (or your agent's skills folder).
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.
allowed-tools
Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply
argument-hint
[narrative-report-path-or-topic]
license
MIT
metadata.author
wanshuiyin/ARIS
metadata.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?
Show full SKILL.md (355 more words)Show less
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-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 /aris-auto-paper-improvement-loop to polish the paper:

/aris-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
markdown
# Paper Writing Pipeline Report

**Input**: [NARRATIVE_REPORT.md or topic]
**Venue**: [ICLR/NeurIPS/ICML/CVPR/ACL/AAAI/ACM/IEEE_JOURNAL/IEEE_CONF]
**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

/aris-idea-discovery "direction"         ← Workflow 1: find ideas
implement                           ← write code
/aris-run-experiment                     ← deploy experiments
/aris-auto-review-loop "paper topic"     ← Workflow 2: iterate research
/aris-paper-writing "NARRATIVE_REPORT.md"  ← Workflow 3: you are here
                                         submit! 🎉

Or use /aris-research-pipeline for the Workflow 1+2 end-to-end flow,
then /aris-paper-writing for the final writing step.

Typical Timeline

PhaseDurationCan sleep?
1. Paper Plan5-10 minNo
2. Figures5-15 minNo
3. LaTeX Writing15-30 minYes ✅
4. Compilation2-5 minNo
5. Improvement15-30 minYes ✅

Total: ~45-90 min for a full paper from narrative report to polished PDF.

© OpenLAIR, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/aris-paper-writing of OpenLAIR/dr-claw.

Open the folder on GitHubat commit d51b64e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in OpenLAIR/dr-claw, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Academic Integrity Rewritelin1111-1/academic-integrity-rewrite102—~1.1kAutomated safety check: PassMIT

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Questions about Aris Paper Writing

What does Aris Paper Writing do?

Workflow 3: Full paper writing pipeline. An agent skill from OpenLAIR/dr-claw. Aris Paper Writing is an agent skill from OpenLAIR/dr-claw. Workflow 3: Full paper writing pipeline.

When should I use Aris Paper Writing?

Aris Paper Writing fits situations like: user says 写论文全流程; write paper pipeline; wants the complete paper generation workflow.

How do I install Aris Paper Writing in Claude Code?

Run `npx skills add OpenLAIR/dr-claw --skill aris-paper-writing -a claude-code`. Or copy the skill folder (skills/aris-paper-writing in OpenLAIR/dr-claw) into .claude/skills/aris-paper-writing in your project. Claude Code loads it when a task matches its description.

How do I install Aris Paper Writing in Codex?

Run `npx skills add OpenLAIR/dr-claw --skill aris-paper-writing -a codex`. Or copy the skill folder (skills/aris-paper-writing in OpenLAIR/dr-claw) into .agents/skills/aris-paper-writing in your project. Codex loads it when a task matches its description.

Can I use Aris Paper Writing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OpenLAIR/dr-claw --skill aris-paper-writing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aris-paper-writing, .gemini/skills/aris-paper-writing, .github/skills/aris-paper-writing and .opencode/skills/aris-paper-writing in your project.

What does Aris Paper Writing need to run?

SKILL.md names no scripts, command-line tools or credentials: Aris Paper Writing is instructions for the agent only. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply.

Does Aris Paper Writing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Aris Paper Writing safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Aris Paper Writing use?

Aris Paper Writing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Aris Paper Writing use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Aris Paper Writing?

Skills that share tags, products or a category with Aris Paper Writing: Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars), Scientific Venue Templates (davila7/claude-code-templates, 33k stars), Econ Write (hanlulong/econ-writing-skill, 651 stars) and NSFC Grant Rationale Writer (huangwb8/ChineseResearchLaTeX, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aris Paper Writing?

OpenLAIR (a GitHub organization) maintains it in OpenLAIR/dr-claw, which has 1,155 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on September 17, 2026.

Source: OpenLAIR/dr-claw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.