Cursor-like Semantic Rules in GitHub Copilot

Both GitHub Copilot and Cursor offer ways to define guardrails for agents in the form of Instructions...

Both GitHub Copilot and Cursor offer ways to define guardrails for agents in the form of Instructions and Rules respectively. On the surface they look the same - just different names for a feature for customizing how AI assistants adapt to your project, be it unit test creation, documentation, or maintaining certain parts of the codebase.

Yet when I turned to GitHub Copilot, I discovered that Instructions are very different conceptually - you define a single file that gets applied to a given repo, folder, or file extensions. In other words, the idea is that you are supposed to (a) have a large .MD file covering lots of topics and (b) rely on relevancy determined by file locations/names.

This approach seems problematic in many ways:

• It's an LLM anti-pattern, bloating the model's context with huge blocks of text without the ability to organize instructions into smaller, targeted documents
• It's not convenient, instruction relevance is determined by file name pattern matching

Cursor's approach seems much better. The official docs propose breaking down Rules into files no longer than 500 lines. Besides, each Rule has a header section (frontmatter metadata) describing the scope of the rule:

---
description: Standards for code quality, linting, and modern API usage in Flutter.
globs: lib/**/*.dart, test/**/*.dart
---
# Flutter Code Quality & Modernization
## 1. Run the Analyzer
After making substantive changes to Dart code, **ALWAYS** run `flutter analyze` to catch errors, warnings, and deprecations.
...

These targeted, small, semantic Rules were something I lacked when switching to GitHub Copilot. I liked how Cursor can match rules based on task in the dialog, not file location. Yet I quickly found an easy workaround - use copilot-instructions.md as a registry of smaller instructions/rules. Besides, it can serve as a shim for existing Cursor rules, making it easier for the coexistence of guardrails used by both AI assistants:

# Nothingness - GitHub Copilot Instructions
This is a Flutter media controller application. Consult the relevant rule files in `.cursor/rules/` when working in their domains.

## Rules Index
| Rule File | When to Consult |
|-----------|-----------------|
| `flutter-best-practices.mdc` | Writing/modifying Dart code. Covers linting, modern APIs, deprecations. |
| `testing-standards.mdc` | Adding features, models, services, widgets, screens. Covers test organization & mocking. |
| `documentation.mdc` | Adding architecture components or complex logic. Covers doc structure. |
| `flutter-commands.mdc` | Running Flutter CLI commands. Covers sandbox permissions. |
| `github-actions-polling.mdc` | Working with CI/CD workflows. Covers polling strategies & failure handling. |
| `rule-creation.mdc` | Creating/modifying rules in `.cursor/rules/`. Covers format & best practices. |

## Agent Behavior
1. **Context efficiency**: Don't load all rules—consult only those relevant to the current task
2. **Run validation**: Always run `flutter analyze` after Dart changes
3. **Reference docs**: Point to existing documentation rather than re-explaining

It turns out modern models fine-tuned for agentic flows are quite curious and tend to follow up on relevant leads they find in the context: