Distributing Agent Instruction Files via APM: Sharing and Pitfalls

Background and Context

A previous sns.style article established that placing a single CLAUDE.md file in a repository root allows Claude Code, Gemini CLI, and GitHub Copilot to read the same instructions simultaneously. The trick works because Claude Code recognizes CLAUDE.md by name, while Gemini CLI and Copilot identify such global instruction files through convention. For small teams with few repositories, writing team conventions directly into that one root file is the cheapest way to stay unified.

As usage deepened, however, the approach began to show its limits. When a team maintains a dozen or dozens of repositories, every new repo requires manually copying and pasting the same CLAUDE.md. Any adjustment to team conventions then forces edits across each repository individually. This repetition is not only slow; missed or incorrect edits produce agents that behave inconsistently from one repo to another.

The author therefore asked whether instruction files could be distributed like code dependencies—packaged as a shared resource that multiple repositories reference. After research, the focus landed on APM, a mechanism originally built for managing package dependencies. By packaging instructions as a dependable artifact, repositories only declare a dependency and automatically pull and apply the unified content, achieving centralized management and one-click updates. This turns scattered metadata into versioned, traceable, reusable engineering assets.

Deep Analysis

Technically, APM distribution relies on clear version numbers and dependency resolution. When a repository declares a dependency on an instruction package, the package manager resolves the specific files to load based on the version and mounts them onto a path the agent can read. Team convention changes then require only bumping the package version; every dependent repository syncs on its next pull without individual edits. This mirrors how frontend teams manage shared component libraries via npm and internal packages through a monorepo, replacing manual consistency with toolchain-enforced consistency. The first practical pitfall is version synchronization. After an instruction package bumps its version, repositories do not perceive the change automatically. Developers must explicitly update the dependency declaration and re-pull, otherwise some repos apply the new convention while others still run the old one. This half-synced state is more dangerous than outright inconsistency, because it creates the false impression that everything is aligned. The second pitfall is override conflicts. When a repository contains its own local CLAUDE.md while also importing a global instruction package via APM, determining priority and merge order becomes complicated. Different agents load local and remote files in different orders—some prioritize local files, others the remote package—so the same repository can behave differently under different tools. Significant effort then goes into figuring out which file wins and how to resolve content conflicts between them.

The third pitfall involves loading paths and mount locations. APM downloads place instruction files in a specific directory, but agents do not share a uniform search path. Some look only in the repository root, while others recurse through subdirectories. As a result, a file may have been downloaded correctly yet remain unread by the agent. These issues reflect a deeper reality of the multi-agent CLI ecosystem: no tool has established a true unified standard for loading instruction files, and each retains its own conventions and boundaries.

Industry Impact

These problems are not isolated to one author; they represent the growing pains any agent toolchain faces while moving toward engineering rigor. As AI coding assistants evolve from personal tools into team infrastructure, sharing and managing instruction files becomes an unavoidable engineering concern. Currently, Claude Code, Gemini CLI, and GitHub Copilot each operate independently, offering uneven support for global configuration and team sharing.

Whoever delivers a cross-tool, cross-repository, versionable instruction-distribution scheme while retaining flexibility will gain the initiative in team collaboration scenarios. For development teams, this means weighing convenience against controllability. Relying solely on each tool's local files makes maintenance cost grow linearly with repository count, while introducing APM adds centralized management at the price of a more complex dependency chain and version-sync burden.

A noteworthy signal is that as agent-tool competition intensifies, third parties or tool vendors may proactively launch cross-platform instruction-sharing standards or hosted services, standardizing what teams currently must discover through trial and error. Until then, this hands-on account remains valuable for documenting the three core pain points.

Outlook

For teams evaluating whether to adopt APM for instruction-file management, the author's experience offers a clear caution. Before acting, teams should first clarify their repository scale, how frequently team conventions change, and the specific loading-order differences among their agent tools. The goal is to avoid unifying for its own sake and thereby overcomplicating a simple problem.

Instruction-file distribution may seem minor, yet it serves as an important yardstick for whether an AI collaboration tool is truly mature enough to support team-level engineering practice. The real-world evidence here—covering version sync, override conflicts, and path differences—provides a reusable reference for other teams attempting similar setups.

Ultimately, the author's account suggests that the immediate future belongs to pragmatic, toolchain-driven consistency rather than hand-maintained files. Teams that map their actual constraints before adopting shared distribution will extract the most value, while those who wait may benefit from the standards and hosted services that competition is likely to produce.