# Understanding Prompt Management in GitHub Repositories: A Call for Best Practices

arXiv: https://arxiv.org/abs/2509.12421 · HTML: https://mlsanity.com/paper/2509.12421/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositories](https://mlsanity.com/paper/2412.17298/) (2024)
- [Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue Resolution](https://mlsanity.com/paper/2501.11709/) (2025)
- [Effects of Prompt Length on Domain-specific Tasks for Large Language Models](https://mlsanity.com/paper/2502.14255/) (2025)

## Built on by (most cited first, 5 of 5)

- [Agent READMEs: An Empirical Study of Context Files for Agentic Coding](https://mlsanity.com/paper/2511.12884/) (2025)
- [PrefPO: Pairwise Preference Prompt Optimization](https://mlsanity.com/paper/2603.19311/) (2026)
- [An Empirical Study of Downstream Adaptation for Agent Skills](https://mlsanity.com/paper/2607.03238/) (2026)
- [BootstrapAgent: Distilling Repository Setup into Reusable Agent Knowledge](https://mlsanity.com/paper/2605.15815/) (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)

## Abstract (arXiv)

The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem.