Research Amp Toolkit: A Set of 15 Claude Code Commands for Structured AI-Assisted Academic Research

This article introduces the open-source Research Amp Toolkit developed by operations research researchers at North Carolina State University. Through 15 Claude Code commands across verification, workflow, content, and classification layers, it addresses three core challenges in AI-assisted academic research: verification difficulties, state loss, and cost control, transforming the vague request "help me do research" into a structured and verifiable academic workflow.

Dilemmas of AI-Assisted Academic Research

As large language models integrate into academic processes, researchers face three major challenges:

1. Verification Difficulty: AI-generated numbers and citations lack efficient verification methods;
2. State Loss: Context dissipates after closing traditional AI conversations, making it hard to track long-term research progress;
3. Cost Control: Research tasks consume a large number of tokens, requiring balanced resource usage.

Project Background and Design Philosophy

This toolkit originated from the doctoral research practice in operations research by Dr. Jake Benhart at North Carolina State University (with over 60 iterations of production-level research conversations). Its core design principles are:

• Text-First Architecture: All outputs are plain text, supporting AI participation, Git traceability, and review-friendliness;
• Adaptable Instead of Blind Application: Allows users to modify prompt files or skip commands, encouraging customization.

Professor Kay stated: "Transparency is structural, not procedural." Text-first is an enforcement mechanism for structural transparency.

The toolkit's 15 commands are divided into a four-layer architecture:

Verification Layer (Ensuring Output Reliability)

• /audit: Citation check, verifying the existence of referenced files and numerical consistency;
• /pcv: Plan-Construct-Verify, including clarification, adversarial review, and manual approval stages;
• /coa: Committee of Experts, analyzing problems from multi-professional perspectives;
• /pace: Parallel Agent Consensus Engine, exposing errors through disagreements.

Workflow Layer (Managing Project Continuity)

• /startup: Restore context and quickly locate the last working position;
• /dailysummary: Generate a daily summary with cross-references;
• /weeklysummary: Aggregate daily summaries into a weekly report;
• /commit: Intelligent Git commit, suggesting reasonable granularity;
• /runlog: Render a tool run log table to track resource usage.

Content Layer (Assisting Content Generation)

• /quarto: Generate Quarto RevealJS slides from background documents;
• /readable: Batch extract text from PDF/Word/HTML, supporting retrieval.

Classification Layer (Intelligent Routing)

• /help: Socratic classification, recommending 1-3 commands;
• /improve: Audit infrastructure and provide improvement suggestions;
• /simplify: Review code/document redundancy and provide refactoring suggestions.

Installation and Configuration

1. Clone the repository: git clone https://github.com/jbenhart44/Research-Toolkit.git
2. Execute installation: cd Research-Toolkit && bash install.sh
3. Configuration file: Edit ~/.claude/toolkit-config.md to set parameters like project name and workflow domain.

Installation Verification

Run cd research-amp/tests/smoke/ && /audit paper.md --sources sources/. The expected output includes 1 verified citation, 1 mismatch, and 1 not found (marking fake citations).

Practical Scenarios

• Citation Check: Use /readable to convert PDF to text → mark citations → /audit for automatic verification → correct errors;
• Multi-Evaluation: /coa to start the Committee of Experts → /pace for parallel evaluation → /pcv to make a plan → /dailysummary to track progress.

Limitations

1. Model Dependency: Designed for Claude Code, some commands rely on its specific capabilities;
2. Learning Curve: The 15 commands take time to learn; although /help assists, active investment is required;
3. Domain Specificity: Biased towards quantitative research/engineering disciplines; more customization is needed for humanities and social sciences.

Future Directions

Version v2 plans to introduce more automated functions and interdisciplinary templates to lower the barrier to use.

Research Amp Toolkit represents a structured paradigm for AI-assisted academic research, positioning AI as a collaborative partner that provides support in verification, planning, execution, and reflection. It does not replace thinking but enhances decision-making capabilities, ensuring reliability through multi-layer verification.

This toolkit provides researchers with a starting point for practical verification. Its open-source nature and text-first architecture support free modification and expansion. More importantly, it demonstrates a responsible way to use AI: prudent trust and enhanced decision-making, which is an attitude urgently needed by the academic community.