mlsanityData to 7 Oct 2026

On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces

arXiv 2608.28497 ↗First posted 28 Aug 2026cs.SE cs.AIcited by 0

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

Abstract (arXiv)

AI coding agents, software tools that automate development tasks through reasoning and tool use, are increasingly extended through plugin marketplaces, yet the structure, maintenance, and co-evolution dynamics of these emerging repositories remain empirically unexplored. Unlike traditional software packages that deliver functionality through source code, agent plugins deliver functionality through a combination of natural-language instruction files, scripts, and configuration files, raising the question of whether these plugins are maintained artifacts that co-evolve across components, or one-off artifacts that developers write once and do not need to revisit. To study the maintenance and co-evolution of agent plugins, we conduct an empirical study of 1,926 repositories hosting Claude Code plugin marketplaces, analyzing 8,351 plugins and 77,773 commits across 2,018 marketplaces. We find that the marketplace is expanding rapidly, plugin-touching commit activity growing 8.8x over six months after the October 2025 launch, and plugins targeting Software Engineering tasks accounting for 61.3% of all plugins. Plugin development is predominantly feature-driven, with feature commits occurring at more than twice the rate of conventional open-source software (OSS) (39.6% vs. 17.2%). Claude co-authors 34.9% of all commits, and four commit types (docs, perf, style, and refactor) carry substantially different meanings in plugin repositories than in traditional software. Most component types evolve independently, but within skills directories, natural-language instruction files and implementation scripts co-evolve at above-chance rates, with 78% of co-changes being functionally coupled, representing a new class of maintenance dependency not observed in traditional software engineering.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 50 references that are in the map. First posted on arXiv 28 Aug 2026.

Builds on

16 of 50 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026Do AI Coding Agents Log Like Humans? An Empirical Study10 Apr
  2. 2026AIDev: Studying AI Coding Agents on GitHub9 Feb
  3. 2026Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity26 Jan
  4. 2025Agentic Refactoring: An Empirical Study of AI Coding Agents6 Nov
  5. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep
  6. 2025On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code18 Sep

Other references · 10

  1. 2026Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionalitycited by 66
  2. 2017An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystemscited by 20
  3. 2024Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Studycited by 16
  4. 2024Exploring ChatGPT App Ecosystem: Distribution, Deployment and Securitycited by 10
  5. 2025Understanding Prompt Management in GitHub Repositories: A Call for Best Practicescited by 5
  6. 2025Extension Decisions in Open Source Software Ecosystemcited by 3
  7. 2022Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystemscited by 2
  8. 2020Using Source Code Density to Improve the Accuracy of Automatic Commit Classification into Maintenance Activitiescited by 1
  9. 2023Are We Speeding Up or Slowing Down? On Temporal Aspects of Code Velocitycited by 1
  10. 2026CommitSuite: A Comprehensive Benchmark for Commit Classification and Message Generationcited by 1

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