How to Configure Repository-Level Custom Instructions for GitHub Copilot

In a longitudinal study of 623 million code changes from 2023 to 2026, GitClear's report, The Maintainability Gap: AI Code Quality in 2026, found that code duplication rose eightfold while refactoring and code reuse plummeted. When developers rely on GitHub Copilot without providing repository-level instructions, the assistant operates on a general understanding of programming languages rather than the specific patterns, architectural decisions, and libraries chosen for that project. This lack of context creates a maintainability gap, where the AI generates functional but disconnected code that violates established team standards.

To resolve this issue, GitHub Copilot supports repository-wide custom rules. Rather than repeating style preferences in every chat session, teams can store these guidelines directly in the codebase. This ensures that every developer, whether using standard code completions or agentic chat modes, works with an assistant that respects the team's engineering decisions.

Repository configurations provide a persistent team handbook. Instead of relying on manual code reviews to catch styling discrepancies, you can define your standards beforehand. Copilot reads these guidelines, correcting its suggestions before they reach your pull requests.

To establish a github copilot repository configuration, you must create a markdown file named copilot-instructions.md inside the .github directory at the root of your project. This specific path, .github/copilot-instructions.md, is the default location that the extension monitors.

The file must be written in standard Markdown and should focus on actionable guidelines rather than high-level philosophies. Since GitHub Copilot appends this file to its system prompt, keeping it concise prevents token bloat. Focus on your language versions, dependency rules, and architectural boundaries.

### Project Guidelines

#### Tech Stack
- Node.js version 20.0.0 or higher
- TypeScript in strict mode
- Vanilla CSS for styling

#### Code Style
- Prefer functional components over classes
- Use standard JSDoc comments for all exported functions
- Avoid deep nesting; return early when conditions are not met

Historically, teams shared these configurations using readme files, local wikis, or generic cloud storage folders in Google Drive or OneDrive. However, maintaining sync across multiple repositories and keeping track of version changes is difficult. Fast.io provides a shared workspaces environment where teams can host, organize, and edit their developer prompt templates and repository configuration files. With full version history, developers can see exactly how coding guidelines evolved over time, ensuring human-agent teams always access the latest instructions.

While a global instruction file is useful for project-wide styling, large codebases often contain distinct languages, tools, and testing frameworks that require different rules. For instance, you would not want the styling rules for your frontend TypeScript files to influence your database migration scripts. To address this, GitHub Copilot supports path-specific instruction files.

Path-specific files can be placed in any directory under the name *.instructions.md. To control where these rules apply, you must include a YAML frontmatter block at the top of the file. This frontmatter supports two primary configuration fields:

• applyTo: Glob patterns specifying which files or directories should receive the instructions.
• excludeAgent: Prevents specific agents (such as code-review or cloud-agent) from using these instructions.

---
applyTo: "src/components/**/*.tsx"
excludeAgent: "code-review"
---
### React Component Instructions
-

Use functional components with typed properties.
- Style elements using Vanilla CSS.

Using glob patterns like src/components/**/*.tsx ensures that these rules only apply to your React views. By setting excludeAgent to code-review, you instruct Copilot to ignore this file during automated code reviews, keeping reviews focused on generic logical checks.

Store your GitHub Copilot instructions in a shared workspace

Keep your copilot-instructions.md and other repo configurations synchronized across human-agent teams. Fast.io provides a secure, versioned workspace to organize and index your developer tools. Start your 14-day free trial today.

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Custom instructions operate on a hierarchical system to prevent conflicts and ensure consistent rule application. At the top level, organization owners on GitHub Copilot Business or Enterprise plans can set global guidelines in the organization settings page. These settings apply to all members and repositories under the organization. However, a local copilot-instructions.md file or a path-specific file takes precedence over organization-wide settings when resolving conflicts. This hierarchy allows teams to maintain general company standards while tailoring repository-specific rules for specialized projects. If a path-specific rule contradicts an organization-wide rule, Copilot will prioritize the path-specific rule for files in that path.

Managing this complexity across multiple development teams requires a central repository of truth. Instead of storing custom instructions in isolated local directories or scattered git repositories, teams can use Fast.io to centralize configuration files in an organization workspace. By enabling Intelligence Mode on a workspace, teams can search across all project guidelines using semantic search or use Metadata Views (which you can learn about on our document data extraction page) to extract schema details, tag prompt templates by department, and track version updates.

Because Copilot reads instructions dynamically, developers must check their instruction files into the active branch during development. During a pull request review, Copilot reads instructions from the head branch. This allows teams to test changes to their coding standards before merging them into the main branch, preventing configuration errors from affecting other team members.

Keep instruction files short and direct. Every line added to a custom instruction file consumes context tokens, which can reduce the size of the codebase Copilot can analyze at one time. Focus on high-value constraints rather than stylistic preferences that your linter already handles. Over-specifying rules can confuse the AI or dilute the importance of primary guidelines. To establish these workflows, you can view our flexible pricing plans and start a 14-day free trial.