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Optimize AGENTS.md by making its instructions discoverable in the coding agent and mode you use, scoped to the right work, clear and non-conflicting, and easy to verify as your repository changes. There is no established universal lint standard, length limit, or guarantee that adding or shortening the file improves agent quality or efficiency. Treat linting as a practical maintenance check—and test behavior in each target harness.
What AGENTS.md optimization means
AGENTS.md is an instruction-file format, not a promise that every coding tool, agent type, or operating mode will load or follow it. Support and discovery differ by product. VS Code lists AGENTS.md as one repository instruction option; Cursor CLI documents reading a root-level AGENTS.md; GitHub documents a Copilot CLI case where built-in subagents do not receive repository instruction files by default.
That variation makes optimization an engineering practice rather than a writing contest. A useful instruction setup should be:
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- Scoped: Instructions apply to the repository, directory, or file patterns they govern.
- Consistent: Rules do not contradict one another or depend on undocumented precedence.
- Actionable: The agent can tell what to do, what constraints matter, and how to verify its changes.
- Maintainable: Commands, paths, and references remain accurate as the repository and tools evolve.
There is no evidence here for a universal “best” file length or token budget. Remove stale or duplicate guidance because it creates maintenance and interpretation problems—not because a particular word count guarantees better results.
#1 Best Overall
First establish what each coding agent loads
Before writing shared repository rules, inventory the actual harnesses you intend to support. Record the product, version, mode, agent type, instruction-file locations, and any known differences between the main agent and subagents. Documentation is product- and mode-specific; do not assume that behavior in one interface applies to every interface from the same vendor.
| Tool and documented case | Instruction support or behavior | Practical implication |
|---|---|---|
| VS Code | Its custom-instructions documentation lists AGENTS.md, .github/copilot-instructions.md, and CLAUDE.md as local instruction choices. It also describes pattern-scoped .instructions.md files. Microsoft cautions that discovery and merging differ across harnesses. |
Choose the supported mechanism and scope for the VS Code workflow in use. Do not rely on an assumed universal file order or precedence rule. Microsoft’s VS Code custom-instructions documentation. |
| GitHub Copilot CLI | GitHub says its built-in explore, task, and code-review subagents do not receive repository instruction files by default. Custom subagents can receive them when configured with include-custom-instructions: true. |
Check subagent configuration separately; do not infer that a built-in subagent inherited the main agent’s repository context. This documented behavior is for Copilot CLI, not every Copilot product. GitHub’s Copilot CLI command reference. |
| Cursor CLI | Cursor’s CLI documentation says it reads root-level AGENTS.md and CLAUDE.md alongside .cursor/rules. Cursor also documents its own rules mechanism. |
Confirm support for the exact Cursor product and mode you use, and decide whether tool-specific rules belong in that product’s mechanism. Cursor’s CLI documentation and Cursor’s rules documentation. |
These examples are not a complete compatibility matrix. Verify the current documentation and behavior for each target harness rather than extrapolating from a similarly named product or another mode.
Choose instruction files by scope, not by fashion
Use a repository-wide file for guidance that genuinely applies across the repository and across the intended agent workflows. Put narrower instructions in nested files or pattern-scoped mechanisms only when the target harness supports them and the narrower scope is useful. A file’s location and name do not prove that a tool discovers it.
When choosing an arrangement, weigh the actual differences among harnesses:
- Support: Does the target tool recognize this file or mechanism?
- Scope: Is the rule repository-wide, nested under a directory, or limited to matching files?
- Merging and precedence: Does the tool document how overlapping instructions are combined? If not, avoid relying on an assumed order.
- Subagent access: Do delegated or built-in agents receive the same instructions, or must that be configured?
- Maintenance cost: Will separate tool-specific files duplicate rules that then drift out of sync?
There is no universally best layout in the documented examples. Prefer the smallest set of supported files that makes scope explicit and does not create unnecessary duplication. Keep product-specific details in a product-specific mechanism when they are not meaningful to other harnesses.
Lint for concrete maintenance defects
Vendors do not publish a shared lint specification for AGENTS.md. A repository can still check for defects that make instructions unreliable. Treat the following as a practical checklist, not as a standardized rule set.
Rank #4
- Stale paths or commands: Do referenced files, scripts, package commands, and setup steps still exist and work?
- Absent tools: Do instructions require a tool, service, or environment that the repository or intended agent does not have?
- Contradictory rules: Do two instructions prescribe incompatible behavior, or does a local rule conflict with a shared one?
- Duplicate guidance: Is the same rule copied across files that can drift? Can it be stated once in a supported shared location?
- Unclear scope: Can a reader or agent tell which files, tasks, or workflows a rule covers?
- Unsupported patterns: Does the target harness actually recognize the nested file or pattern-specific instruction mechanism being used?
- Obsolete references: Do version numbers, links, API names, or repository conventions still match the project?
- Missing verification steps: Does the guidance explain which relevant tests, checks, or review steps should follow a change?
These checks focus on correctness and maintainability. Do not reject a file merely for exceeding an arbitrary length limit: no validated cross-tool maximum is established. Instead, remove material that is demonstrably obsolete, repetitive, ambiguous, or outside the file’s scope.
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Validate instruction discovery and behavior in the real harness
A clean lint result cannot show that an agent loaded the intended file or followed it. For each target combination of tool, mode, and agent type, run a small representative task that exercises one or more important rules. Use a task whose expected constraints and verification steps are clear.
Best Value
- Record the target: Note the tool, version, mode, and agent type, including whether the task uses a subagent.
- Choose a representative change: Select a bounded repository task that triggers relevant guidance, such as a file-specific convention or required test command.
- Check discovery: Inspect available context or tool output where possible to confirm the expected instruction file was loaded. If the interface does not expose this, do not treat silence as proof of discovery.
- Check adherence: Review whether the agent followed the key constraints and used the requested verification steps.
- Fix the cause: If behavior is wrong, investigate discovery, scope, conflicts, or clarity before adding more prose.
- Repeat after change: Re-test when tool versions, configuration, instruction-file locations, or delegation behavior change.
This is a practical validation approach based on documented differences in tool behavior, not a test protocol prescribed by the vendors. Keep results tied to the exact harness and configuration tested.
What published studies can—and cannot—tell maintainers
Research on context files offers reason to evaluate instructions, but not a universal promise. The abstract of Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents? reports broader exploration and instruction adherence in the settings it evaluated. That is a study-specific observation, not proof that every repository or agent will become more correct after adding an AGENTS.md. Read the paper on arXiv.
On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents describes a study spanning 10 repositories and 124 pull requests. Those figures describe the study’s scope; they are not an efficiency improvement or a result that can be applied to every project. Read the paper on arXiv.
Neither summary establishes a validated lint threshold or a tool-independent guarantee of better correctness, speed, or token efficiency. Maintain instructions for clarity and compatibility, then judge their usefulness in the specific workflow where they are loaded.
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