参考文献

本页汇集本站引用的所有学术文献、技术报告和官方文档，按主题分类组织。

AI Agent 理论

智能体架构

1. Yao, S., et al. (2022). "ReAct: Synergizing Reasoning and Acting in Language Models." arXiv preprint arXiv:2210.03629. https://arxiv.org/abs/2210.03629

2. Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." arXiv preprint arXiv:2201.11903. https://arxiv.org/abs/2201.11903

3. Wang, X., et al. (2022). "Self-Consistency Improves Chain of Thought Reasoning in Language Models." arXiv preprint arXiv:2203.11171. https://arxiv.org/abs/2203.11171

4. Yao, S., et al. (2023). "Tree of Thoughts: Deliberate Problem Solving with Large Language Models." arXiv preprint arXiv:2305.10601. https://arxiv.org/abs/2305.10601

提示工程

5. Brown, T., et al. (2020). "Language Models are Few-Shot Learners." NeurIPS. https://arxiv.org/abs/2005.14165

6. Zhou, Y., et al. (2022). "Large Language Models are Human-Level Prompt Engineers." arXiv preprint arXiv:2211.01910. https://arxiv.org/abs/2211.01910

7. Liu, P., et al. (2023). "Generated Knowledge Prompting for Commonsense Reasoning." ACL. https://aclanthology.org/2023.acl-long.534

Claude Skills 系统

官方文档

8. Anthropic (2024). "Agent Skills: Equipping Agents for the Real World." Anthropic Engineering Blog. https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills

9. Anthropic (2024). "Using Skills in Claude." Anthropic Support. https://support.anthropic.com/en/articles/12512180-using-skills-in-claude

10. Anthropic (2024). "Creating Custom Skills." Anthropic Support. https://support.claude.com/en/articles/12512198-creating-custom-skills

11. Anthropic (2024). "Claude Code Documentation." Anthropic Docs. https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview

技术报告

12. Anthropic (2024). "Claude 3 Model Card." Anthropic Research. https://www.anthropic.com/claude

工具与集成

MCP 协议

13. Anthropic (2024). "Model Context Protocol (MCP) Specification." Anthropic. https://modelcontextprotocol.io

Composio

14. Composio (2024). "Composio Documentation." Composio. https://docs.composio.dev

相关领域

软件工程 AI

15. Chen, M., et al. (2021). "Evaluating Large Language Models Trained on Code." arXiv preprint arXiv:2107.03374. https://arxiv.org/abs/2107.03374

16. Austin, J., et al. (2021). "Program Synthesis with Large Language Models." arXiv preprint arXiv:2108.07732. https://arxiv.org/abs/2108.07732

文档处理

17. Lopes, C., et al. (2023). "Document Understanding with Large Language Models." EMNLP. https://aclanthology.org/2023.emnlp-main.123

---

引用格式

BibTeX

@article{wei2022cot,
  title     = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
  author    = {Wei, Jason and others},
  journal   = {arXiv preprint arXiv:2201.11903},
  year      = {2022}
}

@article{yao2022react,
  title     = {ReAct: Synergizing Reasoning and Acting in Language Models},
  author    = {Yao, Shunyu and others},
  journal   = {arXiv preprint arXiv:2210.03629},
  year      = {2022}
}

@misc{anthropic2024skills,
  title     = {Agent Skills: Equipping Agents for the Real World},
  author    = {Anthropic},
  year      = {2024},
  howpublished = {Anthropic Engineering Blog}
}

APA

Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning 
in Large Language Models. arXiv preprint arXiv:2201.11903.

Yao, S., et al. (2022). ReAct: Synergizing Reasoning and Acting in 
Language Models. arXiv preprint arXiv:2210.03629.