A comprehensive ecosystem of AI-driven tools designed to automate the academic research lifecycle from data collection to peer review.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-research-tools
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 ai-research-tools 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 AI Research & Scientific Computing Suite?
The AI Research & Scientific Computing Suite is a robust collection of Openclaw Skills that bridges the gap between raw data and published academic work. It leverages the Model Context Protocol (MCP) to provide standardized access to global academic databases and government data sources, transforming research tasks that typically take months into efficient, minutes-long workflows. By integrating multi-agent systems and specialized replication tools, it allows researchers to focus on core intuition while AI handles the labor-intensive aspects of data gathering, statistical modeling, and document drafting.
This suite supports high-fidelity replication of top-tier journal papers, such as those from the AER, by translating legacy Stata logic into modern Python code while maintaining exact statistical significance. It also includes specialized modules for peer review management and automated slide generation, ensuring that the entire scholarly communication loop is optimized through intelligent automation.
AI Research & Scientific Computing Suite Use Cases
- High-fidelity replication of top-tier academic papers (e.g., AER) using Stata-to-Python translation.
- Rapid literature synthesis and overnight automated research summaries.
- Streamlining the peer review response cycle from several months down to two weeks.
- One-click generation of professional presentation decks from research papers for conference defense.
- Autonomous empirical research and reporting for specific regional markets and policy analysis.
How AI Research & Scientific Computing Suite Works
- Connect to global academic repositories like arXiv and Semantic Scholar using standardized MCP connectors.
- Utilize autonomous research agents to define problems, search for data, and execute statistical analysis.
- Implement specialized replication skills to convert existing methodologies and codebases into executable Python scripts.
- Apply intelligent multi-agent supervision to draft research reports, reducing hallucinations and improving overall quality.
- Automate the generation of supporting materials such as peer review classifications and presentation slides.
AI Research & Scientific Computing Suite Setup
Configure Data Sources (MCP)
# Install the core MCP utility via Openclaw Skills ecosystem
npx clawhub@latest install mcp-tools
# Add specific academic data connectors
openclaw mcp add academic --connector arxiv --connector semantic-scholar
Install Research Tools
# Install the replication engine
npx clawhub@latest install stata-skill
# Install peer review and presentation assistants
npx clawhub@latest install review-response
npx clawhub@latest install paper-slide-deck
AI Research & Scientific Computing Suite Data Schema & Taxonomy
The suite organizes its data through a structured taxonomy to ensure reproducibility and clarity across the Openclaw Skills workflow:
| Data Layer | Component | Description |
|---|---|---|
| Acquisition | MCP Academic Interface | Standardized metadata for journals, citations, and datasets. |
| Analysis | Stata/Python Mapping | Logical equivalence tables for coefficients, standard errors, and p-values. |
| Synthesis | Agent Laboratory Logs | End-to-end audit trails of research steps from hypothesis to report. |
| Output | Review & Slide Schema | NLP-classified review feedback and Markdown-based presentation structures. |
AI Research & Scientific Computing Suite Advanced Features
- Multi-agent Supervisor-Worker architecture to minimize AI errors and ensure cross-validation of results.
- End-to-end autonomous research pipelines via Agent Laboratory with 80% cost reduction.
- Support for Chinese policy and market analysis via specialized DeepAnalyze modules.
- Strategic integration with external frameworks for research design and knowledge management.
- Automated validation of statistical significance thresholds during code translation.
SKILL.md
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