Papers for
tool developers for ai coding assistants
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Agent instruction files vary widely in adoption consistency and upkeep
Toward an Evidence-Based Template for Agent Instruction Files: An Empirical Study of Adoption, Consistency, and Maintenance
Abstract: AI coding agents rely on repository-level instruction files to understand project conventions, development workflows, and constraints. However, different agents support different instruction formats, and limited empirical evidence exists about how these files are adopted, what information they contain, whether coexisting formats provide consistent guidance, and how they are maintained. We conduct an empirical study of 3,387 instruction files from 1,000 GitHub repositories across four formats: AGENTS.md, CLAUDE.md, GitHub Copilot instructions, and Cursor rules. We find that 42.2% of instruction-bearing repositories in the higher-popularity cohort maintain multiple formats. Among 386 manually examined pairs of corresponding sections, 16.1% provide divergent guidance. Our maintenance analysis further shows that 40.4% of the analyzed files have only one recorded modification event and that maintenance patterns differ across formats. Based on category prevalence and category addition ratios (CAR), we derive an evidence-based template containing ten Required, four Gap, and two Recommended categories. In an exploratory generation experiment, explicit template guidance increases structural category coverage from 18.7% to 82.8%. We further introduce AGIT-Instruct, a benchmark for evaluating instruction-file generation through structural coverage and content alignment. Across four generation configurations, retrieval-augmented generation achieves the highest full-file ROUGE-1 F1 of 0.459. A manual assessment of 278 generated category instances finds that 19.4% fully preserve and 61.2% partially preserve developer-written intent. Our findings provide empirical guidance for organizing and maintaining agent instruction
Instruction stacks shape and complicate coding agents’ outputs
Hidden in Plain Sight: an Empirical Study about The Instruction Stacks of Coding Agents
Abstract: Coding agents send their models more than the user's prompt: they add instructions, tool definitions, and environmental context that form an instruction stack. This stack guides execution and changes across releases, yet remains largely unexposed to users and insufficiently characterized in research. We present a longitudinal, cross-agent study of the instruction stacks captured in the first model requests of 1,886 releases of Claude Code, Codex, Gemini CLI, Qwen Code, and OpenCode. Under controlled conditions, we analyze their composition and evolution and trace changes in the four open-source agents to documented motivations. Our findings show why examining the system prompt or reporting the model alone provides an incomplete account of agent inputs. Tool definitions dominate three agents' latest requests, while occasional concentrated revisions alter instructions governing tool use, attribution, and coding style. Such revisions can complicate how researchers identify agent-authored contributions and interpret generated code. We also found that only 48% of examined change units directly explain the motivation for stack revisions. Across agents, shared capabilities emerge despite increasingly distinct wording. These findings expose an evolving, incompletely documented layer of agent design. The findings motivate documenting captured instruction stacks alongside agent releases, models, and user prompts, and preserving the rationale behind their revisions.