An expert research guide designed to mentor scholars through a six-stage workflow for publishing in world-class academic journals.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install academic-paper-mentor
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install academic-paper-mentor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Academic Paper Mentor is a specialized framework within the Openclaw Skills ecosystem designed to function as a senior professor and research guide. Rather than acting as a ghostwriting service, it provides mentorship-style guidance to help scholars refine research questions, design methodologies, and construct logically rigorous frameworks. It is specifically calibrated for high-impact targets such as Nature, Science, Cell, and leading disciplinary journals (Top 1).
By leveraging structured evaluation dimensions and discipline-specific templates, this skill ensures that your academic output meets the highest standards of innovation and feasibility. It supports a wide range of fields including STEM, Medicine, Social Sciences, Management, and Humanities, adapting its internal logic to the unique publishing paradigms of each domain.
To utilize the Academic Paper Mentor within your environment, ensure you have the Openclaw Skills framework active and trigger the mentor using specific keywords.
# Trigger keywords for the skill
# "Academic Writing", "Research Framework", "Thesis Guidance", "Paper Topic"
Ensure your agent has access to the following toolset for optimal performance:
web_search: For real-time literature investigation and DOI retrieval.web_fetch: To extract content from specific research papers.memory: To maintain research progress across multiple sessions.The skill organizes research data through structured reports and taxonomies. Below is the primary evaluation and tracking schema:
| Data Object | Contents | Purpose |
|---|---|---|
| Topic Evaluation Report | Core RQ, Innovation Points, Confidence Index | Selection of the final research direction. |
| Literature Gap Map | Theory Evolution, Conflict Points, 3 Key Gaps | Establishing the research niche. |
| Methodology Matrix | Variable Definitions, Model Choice, Robustness Tests | Ensuring scientific rigor and reproducibility. |
| Logical Architecture | Annotated 3-tier Outlines, Logic Flow Maps | Guiding the full-text drafting process. |
Loading
A secure secret-sharing skill for Openclaw agents that uses the magic-wormhole protocol to transfer sensitive data without exposing it in logs.

A rigorous session initialization protocol designed to synchronize an AI agent's memory, persona, and technical capabilities at the start of every new interaction.

An AI-driven framework for conducting comprehensive due diligence on acquisition targets, covering financial, technical, and operational dimensions.

A comprehensive interface for managing Memberstack accounts, members, and data tables directly from your terminal.

A sophisticated tiered memory management system that optimizes AI agent context windows using a hierarchy of hot, warm, and cold storage tiers.

A production-grade configuration framework that provides AI agents with persistent identity, memory architecture, and rigorous security protocols.








































