drchronx/ai-agent-research-starter-kit

AI Agent teaching and deployment kits for research training, paper writing, and academic workflow automation.

This project organizes commonly used AI Agent capabilities in scientific research into a teachable, replicable, and deployable resource system: from zero beginner introduction, agent environment configuration, Skills usage, to real literature retrieval, knowledge base construction, experimental design, statistical analysis, text mining, EEG/ERP, BCI EEG modeling, paper writing, manuscript review self-check, and journal layout. Suitable for graduate courses, internal group training, personal research workflow construction, and secondary development of AI Agent capability packages.

This repository is a publicly released clean version, excluding original third-party course materials, compressed packages, installation packages, large files, authentic local configuration files, and obvious personal paths.

Who is it for?

• Graduate, undergraduate, and research assistants just beginning to learn AI Agents.
• Researchers who want to use AI Agents in literature reviews, paper writing, data analysis, and project management.
• Teachers or project leaders who need to offer AI Agent research methods courses to students.
• Agent users who want to reuse Skills, prompts, and tutorial cases by ribbon.

What can be done?

• AI Agent Entry-Level Deployment: Comparison of scientific research application scenarios for agents such as OpenClaw, Claude Code, Codex, Hermes, etc.
• Literature and Citations: authentic literature retrieval, BibTeX generation, citation verification, AMiner, AI4Scholar, Zotero, and Obsidian knowledge bases.
• Topic selection and review: CNKI Chinese topic selection, C-journal transfer, literature review, theoretical construction, research question generation.
• Empirical research: scenario experiments, questionnaires and scales, reliability and validity, CFA/EFA, mechanism testing, advanced statistics, and causal inference.
• Data and Text: Data collection, cleaning, visualization, text mining, LLM annotation, variable construction.
• Neuroscience and BCI: EEG/ERP preprocessing, result reporting, neuroneur learning, and deep learning modeling.
• Writing and Publication: Paper structure writing, peer review self-check, rebuttal, journal format conversion, APA 7th edition layout, blind review and anonymization.
• Course teaching: syllabus for each lecture, courseware, classroom tasks, homework, and hands-on tutorials for beginners.

Directory Structure

• 教学资料/: Course syllabus, practical tutorials, courseware, classroom tasks, and assignments organized by lecture time.
• 零基础教学包/: A complete learning path for beginners, including cases, prompt templates, a Skills library, and troubleshooting instructions.
• 综合学术部署包/: Comprehensive deployment manual and functional area skills for research workflows.
• 本地Skills功能分类库/: Local Skills organized by function categories, which can be copied to the Agent workspace as needed.
• SECURITY_CHECK_REPORT.md: Safety inspection and desensitization records prior to this public release.
• release_build_stats.json: Machine-readable statistics of the clean version generation process.

Recommended Learning Paths

1. Read 零基础教学包/00_先读我/README_先读我.md first to understand the overall learning sequence.