How to Standardize AI Coding Rules Across Repositories

To standardize AI coding rules across repositories, keep one canonical source for the shared rules, keep repository-specific rules separate from it, and deliver the shared rules to every repository and every coding agent from that source instead of editing copies. How you deliver them depends on scale: copying a file is enough for a few repositories, a sync script or CI job works when the rules live in Git, and a context layer outside the repositories helps when many repositories and several agents are involved.

This guide is about the repository dimension: what to standardize, which approaches keep ten repositories aligned, and what “consistent” can and cannot mean when the reader of the rules is a language model. The related question of getting Cursor and Claude Code to read the same instructions is covered in sharing project context between Cursor and Claude Code; here it appears as the second axis of the problem.

The problem with repository-by-repository rules

The first repository gets an AGENTS.md or a rules directory. It works, so the next repository gets a copy, and the next. That is a sensible start, and for a handful of repositories it can stay sensible.

The trouble begins when the rules change. Typical symptoms:

• Duplicated rules. The security section is pasted into twelve repositories and edited in three of them.
• Configuration drift. Repository A has the new error-handling convention, repository B has last quarter's, and repository C has a local variation nobody remembers adding.
• Outdated instructions. A rule describes a library the team stopped using, and the agent follows it faithfully.
• Partial updates. Someone fixes the rule in the repository they happen to be working in. Nothing marks the others as stale.
• No authoritative version. When two copies disagree, the answer to “which one is right?” is whoever you ask.
• Different formats per agent. Each repository may also carry a Cursor version and a Claude Code version of the same rule.

None of this is carelessness. Copies diverge because propagating a change to every copy is manual work with no signal for the copies that were missed. The same pattern, from the agency side, is described in managing AI coding context across multiple client projects.

What should actually be standardized?

The goal is not to make every repository identical. Rules have different scopes, and putting a rule in the wrong scope is a common source of noise. A useful split:

• Organization or team-wide standards. Security requirements, logging and error-handling conventions, code review expectations, testing policy, naming, dependency rules. These should read the same everywhere, and are the candidates for a single source.
• Stack or domain conventions. How you structure a Laravel service or a React front end. They apply to the repositories that share the stack, not to all of them.
• Repository-specific rules. Directory layout, the build and test commands, the module that must not be touched. These belong in that repository, next to the code.
• Project and domain context. Specifications, domain terminology, the reasoning behind a design. Detailed reference material is better retrieved when relevant than loaded on every prompt.
• Temporary task instructions. “Migrate this endpoint this week.” These should expire, so they should not live in the same file as permanent rules.

Only the first two are worth standardizing across repositories, and only the first is global. A shared set that is full of repository-specific detail tends to be ignored, or worked around with local overrides, which is another way to get drift.

For the team-level version of what belongs in a shared set and what does not, see managing shared AI coding rules across a development team.

Repositories and agents are two dimensions

Two different problems get described as “sharing rules”, and a fix for one does not fix the other.

• The repository dimension: repository A, B, C. The same standard has to be present in each.
• The agent dimension: Cursor, Claude Code, others. Each reads instructions from its own places and in its own format, although both read AGENTS.md.

You can solve one axis and still pay for the other. A sync script that copies AGENTS.md into every repository removes the per-repository editing, but if half the team also keeps Cursor rules files and a CLAUDE.md, there are still several formats to generate and check. Equally, a team that has made Cursor and Claude Code read the same file in each repository still has that file in every repository.

When you evaluate an approach, ask which axis it covers. Most of the options below cover the repository axis well and say little about agents; the last one is the exception.

Practical ways to standardize rules across repositories

Option 1: copy the same rules into each repository

You write the rules once and paste them into every repository. It needs no tooling, the files are versioned and reviewed with the code, and anyone who clones a repository can read exactly what the agent was told.

Good fit: a small team, a few repositories, rules that change a couple of times a year. If a fix to the shared rules takes ten minutes of repeated edits and happens rarely, this is a fair price for zero infrastructure.

Cost: every change is repeated by hand, and nothing tells you which repositories are behind. It gets worse linearly with the number of repositories and with the rate of change.

Option 2: a shared template or starter repository

New repositories are created from a template that already contains the rules, so every new project starts on the same baseline. This is good for consistency at the moment of creation, and many teams already have a template for CI configuration and linting.

Limitation: a template initializes a repository. It does not update it. After the first commit, each repository owns its copy, and changes to the template never reach the repositories that already exist. Templates solve “start consistently”, not “stay consistent”.

Option 3: synchronize rule files with scripts or CI

Maintain a canonical repository of rules, and distribute them: a scheduled workflow opens pull requests in each repository when the canonical files change, a Git submodule or subtree pulls the directory in, or a script generates agent-specific files from one source.

Advantages: a real single source, changes reviewed as pull requests, and everything stays in Git, with an audit trail. The agents still read ordinary files from the repository, so nothing about their behavior depends on an external service.

Operational trade-offs: you own the automation. Every repository still contains a copy, so there is a window between a rule change and its merge in each repository. Someone has to review dozens of near-identical pull requests, handle repositories that have local edits to the synced files, and decide what happens when a sync fails quietly. Repositories you do not control, such as client repositories, may not accept the automation at all. This is the most robust option for a Git-centric team that is willing to maintain it.

Option 4: shared documentation as the source of truth

Many organizations already have engineering standards in a wiki, a handbook or a Notion workspace. It is tempting to say the standards are already centralized.

There is a difference between documentation existing and the agent receiving the relevant part of it. A page that humans read in onboarding does not influence a coding agent unless something puts the content into its context. Teams close that gap by exporting the documentation into agent instruction files (back to Option 3), by pointing the agent at the pages through a connector, or by pasting excerpts into prompts. Documentation is a good place to write standards. It is a delivery mechanism only when something delivers it.

Option 5: an agent-independent context layer

The last approach keeps the shared rules in a system that is neither a repository nor one agent's configuration, and makes the relevant context available to each agent when it works. Repositories carry only what is specific to them, and a new agent means a new connection rather than a new set of files.

Of the five options, this one addresses both axes. It also adds a system to run and trust, the rules are no longer reviewed in the same pull request as the code unless you arrange that, and a layer that is simply unavailable at the moment an agent starts can leave it without the shared rules. It is a good fit when the number of repositories and agents makes the other options noticeable work, and an unnecessary dependency before that. The longer discussion of keeping rules out of repositories is in keeping AI coding agent rules outside your repository.

How to enforce consistent policy across every AI coding agent your team uses

Consistent policy across agents needs two things: a single source of truth for the policy, and a way to supply that policy to each agent in the form it can use. If either is missing you get the situation above, with some agents following an older version or none at all.

Be careful with the word “enforce”. Rules given to a coding agent are instructions in its context. The model reads them and usually follows them, but nothing guarantees it every time. No context tool, XOROX included, can promise that a model will comply with every rule. What you can make consistent is the input: every agent, in every repository, starts from the same version of the policy, and a change reaches them on the same schedule.

If a policy truly must hold, such as “no secrets in the repository” or “all changes need review”, treat the agent instruction as the first line, and back it with checks that do not depend on the model: linters, pre-commit hooks, CI, branch protection, secret scanning and code review. Instructions make compliant output more likely. Checks make non-compliant output fail.

A practical architecture

Whatever tools you use, the shape that scales is the same, and the diagram above shows it:

1. Organization standards are written once, by people who own them, in a form that can be reviewed.
2. They are published to a shared source of truth: a canonical repository, a documentation page that is synced, or a context layer.
3. Each repository adds its own context on top, and only what is genuinely specific to it.
4. Each agent receives the combination, through whichever mechanism it supports, with no agent-specific copy of the shared part.
5. Checks in CI and review cover what must hold regardless of what the model was told.

You can build this with Git alone (canonical repository, sync workflow, generated files) or with a context layer. The architecture is the same; the difference is where the delivery step lives and who maintains it.

When you don't need XOROX

You probably do not need a separate layer if:

• you have one repository, or a few that rarely share rules,
• you use one coding agent,
• the rules change rarely,
• a checked-in AGENTS.md or rules directory already works well for you, or
• the team is comfortable maintaining repository-local configuration, perhaps with a small sync job.

In that situation, the simplest option that keeps your repositories consistent is the right one. Copy the file, or add the sync workflow, and move on.

When a centralized approach starts making sense

The balance tips when several of these are true at once:

• there are many repositories that should follow the same standards,
• multiple developers work across them,
• the team uses more than one coding agent,
• the standards are shared across a company or agency and change regularly,
• some repositories belong to clients, who should not permanently hold the agency's working method, or
• keeping duplicate rule files aligned has become visible, recurring work.

How XOROX approaches this

XOROX is a project context and work layer for AI coding agents. It manages Rulesets (persistent instructions delivered to the agent automatically), Specifications (detailed project knowledge the agent retrieves when it is relevant) and Tasks (the work queue, which can come from XOROX, GitHub or Notion) outside the repository, and makes them available to Cursor and Claude Code. A developer connects a clone by running xorox init, which stores the project credential in user-level configuration and keeps its project files out of Git. Rulesets are read live, so an edit reaches the agent on a following prompt rather than waiting for a commit in each repository.

Applied to this problem, with the current limits stated plainly:

• Both axes. The agent connection is separate from the stored context, so Cursor and Claude Code receive the same Rulesets and a new agent does not mean rewriting them.
• Rulesets belong to one project. There is no workspace-wide shared Ruleset. To keep a single source for rules used by many repositories, create each project's Ruleset from the same file in a connected GitHub repository, or from the same Notion page, and edit it there.
• Updates. A GitHub-backed Ruleset updates when the file changes on push, so the change goes through your normal pull request review. A Notion-backed one updates when a workspace owner or admin refreshes it. See the GitHub and Notion pages.
• No review workflow of its own. XOROX does not provide an approval flow or change history for Rulesets, which is why backing them with a GitHub file is the better choice for shared standards.
• Not a guarantee. It supplies the same context to each agent. It does not ensure that a model follows it, and the rules remain visible to developers and to the model.

In practice, a team with a shared standards file in GitHub creates a XOROX project for each repository, backs each project's shared Ruleset with that same file, and adds a repository-specific Ruleset where needed. Install the command-line tool on macOS or Linux:

curl -fsSL https://xorox.eu/install.sh | sh
xorox init

On Windows (x64), in PowerShell:

irm https://xorox.eu/install.ps1 | iex
xorox init

See Installation and Quickstart for the full flow. For client work, where the repositories belong to someone else, the ownership side is covered in the agency guide.

Frequently asked questions

How do you share AI coding rules across repositories?

Keep one canonical source and distribute from it instead of editing copies. For a few repositories, copying the file is fine. For more, use a sync script or CI job that opens pull requests from a canonical repository, or a context layer that delivers the rules to agents without a copy in each repository.

Should AI coding rules live in the repository?

Repository-specific rules should: build commands, layout, local constraints. They are versioned with the code they describe. Rules that apply to many repositories are better kept in one source and delivered, because a copy in each repository will drift.

How do you keep Cursor and Claude Code rules consistent?

Both read AGENTS.md, so one file per repository can serve both. Where you need more than that, generate agent-specific files from one source or deliver the rules from a layer both agents connect to. The details are in the Cursor and Claude Code guide.

How do you enforce consistent policy across every AI coding agent your team uses?

Maintain one source of truth for the policy and supply it to every agent from that source. That makes the input consistent. It does not make the model's behavior certain, so back any policy that must hold with linters, CI, secret scanning and review.

When should a team centralize AI coding rules?

When copies are costing you: many repositories, several developers or agents, standards that change regularly, or repositories owned by clients. If you have one repository and one agent, a checked-in file is simpler and enough.

Summary

• Standardize only what is genuinely shared. Organization and stack standards go in a common source; repository details, domain context and temporary tasks stay where they belong.
• Repositories and agents are separate axes. Check which one an approach solves.
• Copying, templates, sync jobs, documentation and a context layer all work at some scale. Choose the simplest one that keeps your repositories aligned.
• Consistent policy means consistent input. Guarantee the rules that must hold with checks, not with instructions alone.
• XOROX is one implementation of the context-layer model. If a checked-in file is enough for you, keep it.