An AI-driven research advisor designed to assist graduate students and researchers through the entire lifecycle of academic projects.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install academic-mentor
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-mentor 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 Mentor?
The Academic Mentor skill transforms your AI agent into a sophisticated research advisor capable of navigating the complexities of graduate-level academia. By integrating this module into your Openclaw Skills library, you gain access to an automated system that evaluates research feasibility, generates structured proposals, and provides strategic publication guidance. It is designed to bridge the gap between initial ideation and formal execution, offering a data-driven approach to innovation assessment and methodology refinement.
This tool is particularly valuable for users who need to synthesize high-level academic requirements into actionable plans. Leveraging specialized Openclaw Skills workflows, it ensures that every step—from literature analysis to advisor matching—is handled with professional-grade technical depth. It empowers researchers to focus on their core contributions while the agent manages the structural and administrative aspects of academic progress.
Academic Mentor Use Cases
- Refinement of research ideas and thesis proposals for graduate and PhD students.
- Feasibility assessment and innovation scoring for new or existing research projects.
- Automated generation of comprehensive research proposals and literature reviews.
- Matching students with potential advisors and labs based on research style and field.
- Strategic recommendation of conferences and journals based on impact factor and deadlines.
How Academic Mentor Works
- The agent identifies the user's current research stage and specific needs through targeted conversational inquiry.
- Core project data is gathered, including research questions, methodology, objectives, and background importance.
- The skill executes specialized Python modules such as
assess_researchoranalyze_literatureto process the inputs. - A detailed assessment is generated, providing multi-dimensional scores for Innovation, Feasibility, and Impact.
- The system produces structured deliverables, such as advisor ranking lists, paper outlines, or full research proposal packages.
- The user receives actionable next steps and the option to export generated materials to professional document formats.
Academic Mentor Setup
To integrate this capability into your Openclaw Skills environment, ensure you have Python 3 installed and then run the following command in your terminal:
pip install -e .
No additional environment variables are required for the basic functionality, though active internet access is recommended for literature and conference metadata retrieval.
Academic Mentor Data Schema & Taxonomy
The skill organizes research data through the following structures to ensure clarity and professional output:
| Component | Data Points Included | Output Format |
|---|---|---|
| ResearchProject | Title, Field, Hypothesis, Methodology, Objectives | JSON/Object |
| Research Assessment | Readiness Level, Innovation Score (0-100), SWOT Analysis | Markdown Summary |
| Advisor Match | Name, Institution, Match Score, Contact Strategy | Ranked List |
| Paper Outline | Target Venue, Section-by-section Key Points, Writing Tips | Structured Document |
| Literature Analysis | Trends, Identified Gaps, Citation Assessment | Analysis Report |
Academic Mentor Advanced Features
- Automated generation of full-length research proposals including abstracts, timelines, and reference sections.
- Multi-dimensional scoring engine that evaluates research readiness across five distinct academic dimensions.
- Intelligent advisor matching algorithm that filters for location, institutional prestige, and current acceptance status.
- Venue-specific paper outlining that tailors suggestions to specific top-tier conferences like ICML or NIPS.
- Real-time literature gap identification to help students find novel angles for their thesis work.
SKILL.md
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