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To help a coding agent work reliably in a repository, document the project-specific facts it cannot safely infer from code: its architecture, important directories, verified commands, conventions, security requirements, and completion checks. Put those facts in an instruction format the agent’s harness actually discovers, then test the guidance on a representative task. An instruction file can improve orientation, but its presence alone does not guarantee better results.
Start with a recurring failure, not a generic rule list
Identify a concrete problem first: perhaps the agent repeatedly adds features to the wrong module, uses an unsupported library, skips the relevant tests, or misses a compatibility requirement. Choose a representative task that exposes that problem and define observable success criteria before editing instructions.
For that task, note which files should change, which project conventions matter, which commands should run, and what corrections a maintainer currently has to make. This gives you a baseline to compare against. Microsoft’s VS Code guidance on configuring AI for a codebase recommends establishing a baseline with a representative task.
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Instruction formats are not universal. Support and discovery depend on the selected agent harness and session, so use the documented mechanism for the tool your team actually runs and confirm its current behavior before rollout.
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| Harness documented by VS Code | Instruction format listed | Scope notes |
|---|---|---|
| OpenAI Codex | AGENTS.md |
Codex can use subfolder AGENTS.md files for targeted guidance. |
| Anthropic Claude | CLAUDE.md |
Claude rules can use paths for targeted application. |
| GitHub Copilot | .github/copilot-instructions.md or AGENTS.md |
VS Code also documents targeted .instructions.md files with applyTo patterns. |
These formats and mechanisms are those in VS Code’s documented guidance; they should not be taken as a promise that every version, IDE, agent, or configuration handles them identically. See Microsoft’s custom-instructions reference for the stated support.
Put hard-to-infer project knowledge in the guide
Project instructions are most useful when they capture decisions the agent cannot reliably reconstruct from reading code. OpenAI describes AGENTS.md files as applying to the directory tree rooted where each file lives, with deeper files taking precedence in conflicts; resolution details remain tool-specific. See the OpenAI Codex guide to AGENTS.md.
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Explain the project and repository map
State what the software does and identify the major components. Map the directories where application code, tests, examples, and documentation live. Point out architectural boundaries and explain where new functionality belongs, especially when a directory name alone does not reveal its purpose.
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Include the install, build, test, lint, and formatting commands maintainers have checked, along with the working directory where each command should run. Say which checks apply to which kinds of changes. A command copied from an old guide or run from the wrong package can send an agent in the wrong direction.
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Record conventions and constraints
Describe project-specific expectations for naming, error handling, API compatibility, security, dependencies, and testing. Explain important interfaces and dependencies that should not be bypassed. Avoid restating generic advice that an agent already receives or rules that are fully documented in another source of truth.
Define completion checks
Say what a complete change means: for example, which relevant tests or static checks should pass, and whether documentation or examples need updating. Keep the checks conditional where appropriate instead of giving every change an indiscriminate, lengthy checklist.
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Decide what belongs at the root and what should be scoped
Use a root guide for facts that apply across the repository. Put a rule closer to the relevant code when it applies only to a language, package, module, or file pattern—and only when the chosen harness supports that targeted mechanism.
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| Placement | Best for | Watch for |
|---|---|---|
| Root instruction file | Project purpose, repository map, shared architecture, common commands, and repository-wide conventions. | Overloading it with details that apply to only one part of the codebase. |
| Nested or targeted instructions | Rules specific to a directory, language, package, or matching files. | Whether the harness discovers the file and how overlapping instructions are resolved. |
For Codex, OpenAI documents directory-tree scope and precedence for nested AGENTS.md files. VS Code documents subfolder AGENTS.md for Codex and targeted instruction mechanisms for other supported configurations. If more than one instruction source may apply, verify the effective guidance in the tool rather than assuming precedence is the same everywhere.
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Keep instructions accurate, concise, and nonduplicative
Review each statement against the repository. Confirm that referenced paths exist, commands work from the stated directory, architecture descriptions match the current code, and conventions are actually followed. Remove generic, obsolete, duplicated, or speculative rules. Generated or edited instructions still need maintainer review.
If a team uses several agent harnesses, share a supported format where practical. If separate files are necessary, keep the underlying project facts consistent and make scope clear so the same rule is not contradicted in different places.
Test whether the guidance changes outcomes
- Record the baseline. Run the representative task without the new guidance, using the same repository and comparable context and settings you will use in the follow-up. Note changed files, convention adherence, commands run, and corrections needed.
- Add only relevant guidance. Write the specific project fact that addresses the recurring failure. Keep cross-project facts in the root guide and use supported scoped instructions for local rules.
- Review the content. Check paths, commands, architecture, technology choices, conventions, and the definition of done for accuracy.
- Confirm discovery. Start a fresh session with the intended harness and repository, then check that the expected instruction file is loaded. VS Code distinguishes this from adherence: “This checks discovery, not whether the agent follows every instruction.”
- Repeat and compare. Give the agent the same representative task under comparable conditions. Compare files changed, use of conventions and libraries, commands run, and the corrections still required.
- Revise based on the gap. If the file loads but the agent still misses a rule, make the relevant guidance clearer or better scoped, then repeat the comparison. Treat successful discovery and successful following as separate observations.
What a successful test can—and cannot—show
A better result on one representative task is evidence that the instructions helped in that context, not proof that they improve every task. The official guidance cited here does not establish a general performance effect size or a universal improvement percentage. Keep the claim tied to the harness, repository, task, and conditions you actually evaluated.
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