mlsanityData to 14 Oct 2026

On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code

arXiv 2509.14744 ↗First posted 18 Sep 2025cs.SEcited by 14

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write/execute the actual code with minimal human intervention. Key to this process are agent manifests, configuration files (such as Claude.md) that provide agents with essential project context, identity, and operational rules. However, the lack of comprehensive and accessible documentation for creating these manifests presents a significant challenge for developers. We analyzed 253 Claude.md files from 242 repositories to identify structural patterns and common content. Our findings show that manifests typically have shallow hierarchies with one main heading and several subsections, with content dominated by operational commands, technical implementation notes, and high-level architecture.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 16 references that are in the map. First posted on arXiv 18 Sep 2025.

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12 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild14 Jun

Other references · 11

  1. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  2. 2024The Prompt Report: A Systematic Survey of Prompt Engineering Techniquescited by 121
  3. 2024Can LLMs Replace Manual Annotation of Software Engineering Artifacts?cited by 42
  4. 2024AutoDev: Automated AI-Driven Developmentcited by 24
  5. 2024On the Impacts of Contexts on Repository-Level Code Generationcited by 22
  6. 2018Categorizing the Content of GitHub README Filescited by 18
  7. 2025Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Codingcited by 11
  8. 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
  9. 2025Knowledge Graph Based Repository-Level Code Generationcited by 4
  10. 2025The Introduction of README and CONTRIBUTING Files in Open Source Software Developmentcited by 3
  11. 2023Exploring the intersection of Generative AI and Software Developmentcited by 2

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Citing papers per year: 2025: 2; 2026: 12.

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  1. 2025Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects
  2. 2026Harness Engineering for Agentic AI Coding Tools: An Exploratory Study
  3. 2026An Exploratory Study of Agent Plans for Agentic AI Coding Tools in Open-Source Software
  4. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
  5. 2026Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
  6. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
  7. 2026A Dataset of Agentic AI Coding Tool Configurations
  8. 2025Agentic Refactoring: An Empirical Study of AI Coding Agents