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Put durable project context in a repository instruction file, add narrowly scoped rules only where they are needed, and use verified workflow controls for anything that must be enforced. For GitHub Copilot, the shared starting point is .github/copilot-instructions.md. Path-specific files ending in .instructions.md can refine that guidance for selected directories, while hooks or CI checks—not prose—are the appropriate place for deterministic actions.
The exact files Copilot reads depend on the product surface: IDE, CLI, cloud agent, or code review. Confirm support in the documentation for the surface and version your team uses. The setup below is therefore Copilot-specific where filenames and discovery behavior are concerned, and vendor-neutral where it describes design principles.
What repository instructions do—and do not do
Repository instructions are durable context supplied to an AI coding agent whenever it works in a project. They explain how the codebase is organized, which conventions matter, and how a change should be built, tested, and checked. GitHub describes repository custom instructions as context for Copilot rather than as a security boundary. See GitHub’s response-customization documentation for the supported surfaces and current behavior.
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- Use instructions for understanding and consistency: architecture notes, naming rules, directory ownership, testing commands, generated-file policies, and review expectations.
- Use deterministic controls for enforcement: CI status checks, protected branches, permission boundaries, sandboxing, secret scanning, or a verified hook that runs a command at a defined workflow point.
- Do not promise that a sentence can prevent an action. An agent may misunderstand, omit, or incorrectly apply prose. A required check must be implemented and tested as a check.
GitHub lists hooks as a separate customization mechanism: custom shell commands executed at specified workflow points. Their availability and execution semantics must be checked for the particular Copilot product and version you deploy; do not assume that a hook available in one surface exists in another. The distinction is summarized in the Copilot customization cheat sheet.
Choose the instruction scope before writing rules
Start by deciding whether a rule applies everywhere, only to a path, or only to a particular task or workflow. Scope controls both usefulness and maintenance cost.
| Mechanism | Best use | Scope | Important qualification |
|---|---|---|---|
| Repository-wide instructions | Shared project context and conventions | All applicable repository requests | Copilot support varies by surface; verify the target documentation. |
Path-specific *.instructions.md files |
Rules that genuinely differ by language, component, or generated area | Files matched by an applyTo glob |
GitHub.com documents this support for Copilot cloud agent and code review; other surfaces may differ. |
| Prompt files | Reusable task wording, such as a release-note or migration request | A selected task | They are not a replacement for project-wide context. |
| Custom agents or skills | Specialized roles and repeatable capabilities | A selected agent or capability | Availability and syntax are product-specific. |
| Hooks and CI controls | Commands, checks, and workflow actions that must run | A defined event or pipeline stage | Use only after confirming the mechanism and its permissions in your environment. |
Keep shared rules in one broad file and add a narrower file only when the narrower scope represents a real difference. Splitting identical guidance across many files makes updates harder and increases the chance of contradictions.
Step 1: Create the repository-wide Copilot file
For GitHub Copilot, create a Markdown file at the repository root:
.github/copilot-instructions.mdGitHub treats this file as repository-wide context. Write it in natural-language Markdown, and commit it so the instructions travel with the project. The file should tell an unfamiliar agent what it needs to know to make and validate a safe change, not attempt to restate every file in the repository.
What to put in the file
Derive the content from the repository itself. Do not invent commands or conventions. A useful structure is:
- Project purpose: one short paragraph describing what the software does and which areas are in scope.
- Directory map: identify the major application, test, infrastructure, documentation, and generated-code directories, with the role of each.
- Architecture boundaries: record layering rules, public interfaces, dependency direction, and places where a change requires an owner’s review.
- Build and test commands: list the commands that the repository actually documents or uses in automation. Include the working directory and any required environment setup.
- Formatting and linting: name the configured formatter, linter, type checker, and their expected invocation.
- Change-validation expectations: state which checks are required for a small change, a schema change, a dependency update, or an API change.
- Generated and vendored files: explain which files must be regenerated, which are never edited manually, and which directories are excluded from routine changes.
- Operational constraints: describe migration safety, backward compatibility, feature flags, logging or privacy rules, and how secrets are handled.
- Completion behavior: ask the agent to report files changed, commands run, results, and any checks it could not run.
These are practical recommendations based on the documented purpose of repository instructions; GitHub does not mandate this exact checklist. The correct commands and policies are the ones established by your repository.
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Example repository-wide file
The following is a format example. Replace every project-specific statement with facts from the target repository before committing it:
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# Repository guidance for Copilot
## Project map
- `src/` contains application code.
- `tests/` contains automated tests.
- `docs/` contains user and API documentation.
- `generated/` is produced by the repository's generator and is not edited by hand.
## Before changing code
- Read the module's existing tests and neighboring implementation.
- Preserve the public API unless the task explicitly requests a breaking change.
- Follow the formatter and linter configuration already committed to this repository.
## Validation
- Run the documented unit-test command for the affected package.
- Run the documented lint and type-check commands when source files change.
- Report each command and whether it passed, failed, or was unavailable.
## Dependencies and secrets
- Do not commit credentials, tokens, private keys, or local environment files.
- Do not add a dependency when an existing repository dependency provides the required capability.
## Generated files
- Change the generator input, then run the repository's documented generation command.
- Do not hand-edit generated output.This example intentionally avoids fabricated commands. A real file should contain the exact commands and paths used by your project.
Step 2: Add path-specific instructions where the rules really change
GitHub documents modular instruction files ending in .instructions.md under .github/instructions/. They use YAML frontmatter with an applyTo glob. A matching file can be applied along with the repository-wide instructions. See GitHub’s repository-instructions guide for current syntax and feature support.
Use this mechanism for genuine differences, such as a Python service with one validation policy and a frontend with another, or a generated directory that needs special handling. Keep the pattern narrow enough that you can explain exactly which files it matches.
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---
applyTo: "src/**/*.py"
---
Follow the Python conventions documented in this repository. For changes under this path, read the neighboring tests and run the repository's documented Python validation command. Do not modify generated files directly.Designing reliable applyTo patterns
- Match the smallest meaningful subtree instead of the whole repository.
- Prefer one clear rule per concern; do not create overlapping files that give conflicting instructions.
- Check whether the glob matches tests, examples, generated output, and configuration files as well as production code.
- Keep the broad file responsible for shared policy and the path-specific file responsible for the exception or specialization.
- Review path-specific files whenever directories are renamed or code is moved.
GitHub’s documentation provides glob examples, but it does not establish that one pattern is correct for every repository. Verify the matches in the product surface you use.
Step 3: Verify what the Copilot surface actually loads
Do not assume that a file supported in one Copilot experience is automatically read everywhere. GitHub documents different support for IDE features, Copilot CLI, cloud agent, and code review. Before relying on a rule, check the documentation for the exact surface, account configuration, and product version.
Copilot CLI discovery
Copilot CLI combines applicable instruction files from its documented repository and user locations. Its documentation does not define a universal precedence order among all files. When behavior is surprising, inspect the files discovered for the current session rather than guessing which one won.
- Start Copilot CLI in the repository or subdirectory where the task will run.
- Use the
/instructionscommand to view the instruction files discovered for that session. - Enable or disable an individual file when diagnosing an unexpected response.
- Resolve contradictory wording in the files, then inspect discovery again before relying on the result.
The CLI also documents nested and parent repository locations and gives modular *.instructions.md files a matching role. Treat those discovery rules as CLI-specific; they are not a universal convention for every AI coding agent.
IDE, cloud-agent, and code-review differences
GitHub’s customization documentation is the authority for which file types each surface currently supports. A rule that works in an IDE may not be loaded by a cloud agent, and a path-specific file documented for GitHub.com may not be recognized by a local client. Record the intended surface in your team documentation and test a representative task there.
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Step 4: Turn “guardrails” into testable controls
Instructions can tell an agent to run a test or avoid a secret, but they do not prove that the test ran or that a secret was not committed. Build a separate control for each requirement whose failure would matter.
| Requirement | Instructional guidance | Deterministic control to evaluate |
|---|---|---|
| No credentials in commits | Tell the agent never to add secrets or local environment files. | Secret scanning, pre-commit checks, and CI policies configured for the repository. |
| Tests must pass | List the exact affected-area commands. | Required CI status checks and protected-branch rules. |
| Generated code is reproducible | Tell the agent to edit inputs and regenerate output. | A CI job that regenerates and fails when the working tree changes. |
| Only approved dependencies are added | Explain existing libraries and approval expectations. | Dependency review, allowlists, or an approval workflow verified in the target platform. |
| Production access is restricted | Tell the agent which environments are off limits. | Repository permissions, isolated credentials, sandboxing, and deployment gates. |
Hooks may be suitable for a command that must run at a defined workflow point, but confirm the available hook events, process permissions, failure behavior, and logging before treating a hook as a control. A shell command that merely exists in a file is not evidence that it ran successfully.
Write instructions that support, rather than impersonate, enforcement
Use precise, observable language:
- “Before editing the schema, read
docs/migrations.mdand add or update the corresponding migration test.” - “After changing files under
src/payments/, run the documented payments test command and report its exit status.” - “Never print, copy, or commit values from secret-bearing environment variables.”
Then implement the required CI, permission, or scanning control separately. If the control is not configured, describe the instruction as guidance—not as a guarantee.
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Keep rules short enough to use
Agents perform better when high-value constraints are easy to find. Put stable project facts and commands near the top, remove historical commentary, and link to deeper internal documentation when a policy needs a full explanation. Avoid repeating the same rule in the broad file and every path-specific file; repetition creates opportunities for drift.
Use one source of truth for commands
If the repository already defines commands in a task runner, package configuration, Makefile, or CI workflow, reference that source rather than copying a second, potentially stale command. When a command changes, update the source and the instruction that points to it in the same pull request.
Explain exceptions explicitly
A broad rule such as “run all tests” may be impractical for a documentation-only change. State the repository’s real decision rule: which checks are required for which paths, and when a full suite is mandatory. This gives the agent a usable choice without weakening the policy.
Review instruction changes like code
- Require normal code review for changes under
.github/. - Ask the author to demonstrate which paths a new
applyTopattern matches. - Check that commands still exist and that examples reflect current tooling.
- Include instruction-file changes in release or migration notes when they alter agent behavior.
- Remove obsolete rules instead of leaving contradictory historical advice.
Copilot code review has a branch-specific consequence
GitHub says Copilot code review reads repository custom instructions and skills from the pull request’s head branch. Therefore, when a pull request changes an instruction file, that change can affect the review of the same proposed branch. The behavior is documented in GitHub’s Copilot code-review guide.
For teams, this means instruction files are part of the change under review, not an invisible global setting. Reviewers should inspect the new guidance, its scope, and any altered validation expectations alongside the application code. Do not generalize this branch behavior to other products without product-specific evidence.
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A practical rollout and verification plan
Use a small, observable rollout rather than writing a large rulebook in one commit.
- Inventory the repository. Identify the real build, test, lint, type-check, generation, and deployment commands; map major directories and note existing automation.
- Write the smallest useful broad file. Include project purpose, directory roles, architecture boundaries, validation commands, generated-file policy, and secret-handling expectations.
- Add only justified path-specific files. For each file, document why the broad guidance is insufficient and verify the glob’s matches.
- Inspect discovery. In Copilot CLI, run
/instructionsand resolve conflicts. For other surfaces, use the applicable GitHub documentation and a representative task. - Run a low-risk pilot. Ask the agent to make a small change in a path covered by the instructions. Check whether it follows the directory conventions, uses the expected commands, and reports validation accurately.
- Test failure paths. Confirm what happens when a command is unavailable, a test fails, a generated file is edited, or a rule conflicts. Improve wording and controls based on observed behavior.
- Add enforcement separately. Configure required CI checks, branch protection, secret scanning, permissions, or verified hooks for requirements that cannot rely on agent cooperation.
- Document the supported surfaces. State whether a file is intended for IDE use, CLI, cloud agent, code review, or a combination, and link to the current product documentation.
- Review continuously. Update instructions when the repository’s commands, architecture, directories, or security controls change.
Common failure modes and fixes
The agent ignores a rule
First confirm that the file is supported by the surface and discovered for the current session. In CLI, inspect /instructions. Then look for contradictory files, an overly broad or overly narrow glob, and wording buried under unrelated detail. If the behavior must be guaranteed, move the requirement to CI, permissions, scanning, or another verified control.
A path-specific rule applies to the wrong files
Inspect the glob against real paths, including tests, examples, generated output, and nested directories. Narrow the pattern and add a specific exception or reorganize the file boundaries. Do not assume a glob example from documentation matches your repository layout.
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Copilot CLI documentation does not define a universal precedence order. Remove the contradiction instead of relying on an assumed priority. Put the shared policy in the broad file and leave only the true specialization in the path-specific file.
The agent reports a command that was never run
Require a command-and-result report in the instructions, but verify execution through CI logs, a hook with known behavior, or another observable workflow record. A textual claim is not a test result.
A “security guardrail” is only a paragraph
Keep the paragraph as context, then add the actual boundary: least-privilege credentials, isolated execution, branch protection, secret scanning, dependency review, or a deployment approval gate. Validate that the control blocks the undesired action in the environment where the agent operates.
Instructions become stale
Schedule instruction review with changes to build tooling, directory layout, test commands, or security policy. Prefer references to existing configuration over duplicated command lists, and delete rules that no longer describe the repository.
How to handle other agents and filenames
Names such as AGENTS.md, CLAUDE.md, and GEMINI.md are used by some tools, but the available evidence here establishes neither identical discovery rules nor identical precedence across products. Do not copy Copilot’s filenames or behavior to another agent without checking that agent’s current official documentation. The general design still transfers: define scope, keep guidance close to the code it governs, inspect what is loaded, and enforce critical requirements outside prose.
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Recommended repository layout
A compact Copilot-oriented layout might look like this:
repository-root/
├── .github/
│ ├── copilot-instructions.md
│ └── instructions/
│ ├── python.instructions.md
│ └── generated.instructions.md
├── src/
├── tests/
├── docs/
└── generated/The layout is illustrative, not a requirement. The files and directories should exist only when they match your repository and the Copilot surface you support.
Final decision framework
- If the agent needs project facts or conventions, put them in repository-wide instructions.
- If a rule applies only to a real subset of files, use a narrowly matched path-specific instruction file where the target Copilot surface supports it.
- If a task is reusable but not universal, consider a prompt file, custom agent, or skill supported by your product.
- If an action must happen or must be blocked, implement and verify a hook, CI check, permission boundary, or other deterministic mechanism.
- If behavior is unexpected, inspect discovered files and remove conflicts before adding more prose.
- If you use Copilot code review, remember that its custom instructions and skills come from the pull request head branch.
Frequently Asked Questions
Where should a GitHub Copilot repository instruction file live?
For repository-wide Copilot context, create .github/copilot-instructions.md at the repository root. Confirm that the Copilot surface you use supports this file.
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No. They provide context and guidance. Use permissions, isolated credentials, secret scanning, CI checks, protected branches, or verified hooks for requirements that must be enforced.
How do I see which instructions Copilot CLI loaded?
In Copilot CLI, run /instructions to view discovered files and enable or disable individual files for the current session.
Do path-specific instruction files work in every Copilot feature?
No. GitHub documents path-specific support for Copilot cloud agent and code review, while support in other surfaces can differ. Check the documentation for the exact product and version.
Which branch’s instructions does Copilot code review use?
GitHub says Copilot code review reads repository custom instructions and skills from the pull request’s head branch.
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