A professional 12-step AI workflow for systematic academic paper revision and critical peer review within Openclaw Skills.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install academic-paper-refinement
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install academic-paper-refinement using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Academic Paper Refinement?
Academic Paper Refinement is a sophisticated technical framework designed to take academic manuscripts from rough drafts to submission-ready final versions. By integrating a multi-agent ecosystem—including EvoScientist, AutoResearchClaw, and specialized peer-reviewers—this skill automates deep structural analysis, data verification, and linguistic polishing. It is an essential tool for researchers using Openclaw Skills who require rigorous, multi-perspective feedback to meet the high standards of top-tier journals and conferences.
The skill operates on a principle of critical skepticism, simulating the perspective of a harsh reviewer to identify fatal flaws before actual submission. It handles the entire lifecycle of paper refinement, including LaTeX source optimization, automated PDF generation, and bibliography management according to CCF standards. This systematic approach ensures that every claim is supported by evidence and that the paper's presentation is flawless.
Academic Paper Refinement Use Cases
- Critical peer review simulation where multiple agents identify potential rejection reasons to strengthen the manuscript's arguments.
- Rigorous data consistency auditing to ensure numbers match perfectly across abstract, text, tables, and figures.
- Language humanization to refine AI-generated content into natural academic prose while maintaining strict causal integrity.
- Automated bibliography cleanup and CCF reference ranking to ensure citations meet high-quality publication standards.
How Academic Paper Refinement Works
- Performs initial journal format analysis by examining recent publications and restructuring the LaTeX source accordingly.
- Conducts a deep data audit to verify numerical consistency and statistical reporting completeness.
- Executes three distinct rounds of critical reviews using four integrated AI research agents to score the paper's merit.
- Iteratively revises the manuscript based on a prioritized issue list (P0 to P2) generated from combined reviewer feedback.
- Triggers final optimizations, including content compression for page limits, language humanization, and a 4-step LaTeX compilation process.
Academic Paper Refinement Setup
To utilize this skill, ensure your environment has the necessary document processing utilities and binary paths configured.
# Install system dependencies
sudo apt-get install pandoc wkhtmltopdf poppler-utils
# Verify path availability for core tools
# /usr/local/bin/EvoScientist
# /usr/local/bin/researchclaw
Academic Paper Refinement Data Schema & Taxonomy
The skill manages its outputs through a strictly versioned directory structure, ensuring a clear audit trail for the refinement process.
| Directory | Purpose |
|---|---|
v0/ - v1/ |
Initial structural cleanup and data verification reports. |
v2/ - v4/ |
Intermediate revision versions based on Round 1 and Round 2 reviews. |
humanlike/ |
The version processed by the Humanizer agent for natural flow. |
v_final/ |
The final production-ready LaTeX source and PDF after technical cleanup. |
reviews/ |
Consolidated MD reports from all agents (EvoScientist, ARC, etc.) for each round. |
Academic Paper Refinement Advanced Features
- Multi-Agent Consensus Scoring: Aggregates scores from 4 different AI reviewers to provide a reliable acceptance probability estimate.
- Automated Causal Control: Ensures that language polishing doesn't accidentally introduce prohibited causal claims (e.g., 'led to' vs 'associated with').
- Page Limit Compression: Intelligent content reduction that preserves core contributions while meeting strict publisher page counts.
- One-Key LaTeX Verification: Automated shell script that handles pdflatex and bibtex cycles to eliminate unresolved references or '??' markers.
- CCF Quality Filtering: Automatically ranks and validates references against the China Computer Federation catalog.
SKILL.md
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