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

arXiv: https://arxiv.org/abs/2509.14744 · HTML: https://mlsanity.com/paper/2509.14744/

## 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.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 12 of 12)

- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [The Prompt Report: A Systematic Survey of Prompt Engineering Techniques](https://mlsanity.com/paper/2406.06608/) (2024)
- [Can LLMs Replace Manual Annotation of Software Engineering Artifacts?](https://mlsanity.com/paper/2408.05534/) (2024)
- [AutoDev: Automated AI-Driven Development](https://mlsanity.com/paper/2403.08299/) (2024)
- [On the Impacts of Contexts on Repository-Level Code Generation](https://mlsanity.com/paper/2406.11927/) (2024)
- [Categorizing the Content of GitHub README Files](https://mlsanity.com/paper/1802.06997/) (2018)
- [Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding](https://mlsanity.com/paper/2503.16508/) (2025)
- [Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild](https://mlsanity.com/paper/2506.12347/) (2025)
- [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025)
- [Knowledge Graph Based Repository-Level Code Generation](https://mlsanity.com/paper/2505.14394/) (2025)
- [The Introduction of README and CONTRIBUTING Files in Open Source Software Development](https://mlsanity.com/paper/2502.18440/) (2025)
- [Exploring the intersection of Generative AI and Software Development](https://mlsanity.com/paper/2312.14262/) (2023)

## Built on by (most cited first, 14 of 14)

- [Agent READMEs: An Empirical Study of Context Files for Agentic Coding](https://mlsanity.com/paper/2511.12884/) (2025)
- [AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents](https://mlsanity.com/paper/2604.02947/) (2026)
- [Agentsway -- Software Development Methodology for AI Agents-based Teams](https://mlsanity.com/paper/2510.23664/) (2025)
- [A Practical Guide to Agentic AI Transition in Organizations](https://mlsanity.com/paper/2602.10122/) (2026)
- [Codified Context: Infrastructure for AI Agents in a Complex Codebase](https://mlsanity.com/paper/2602.20478/) (2026)
- [Knowledge Activation: AI Skills as the Institutional Knowledge Primitive for Agentic Software Development](https://mlsanity.com/paper/2603.14805/) (2026)
- [From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintained](https://mlsanity.com/paper/2607.00911/) (2026)
- [Configuration Smells in AGENTS.md Files: Common Mistakes in Configuring Coding Agents](https://mlsanity.com/paper/2606.15828/) (2026)
- [Evidence-Calibrated Runtime Reconstruction for Agent Skills Across Heterogeneous Coding Agents](https://mlsanity.com/paper/2608.08793/) (2026)
- [AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent](https://mlsanity.com/paper/2604.06296/) (2026)
- [Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment](https://mlsanity.com/paper/2606.11672/) (2026)
- [From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation](https://mlsanity.com/paper/2608.20195/) (2026)
- [On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces](https://mlsanity.com/paper/2608.28497/) (2026)
- [Specifying and Maintaining Agentic Workflows: An Empirical Study of GitHub Agentic Workflows](https://mlsanity.com/paper/2609.27263/) (2026)

## 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.