feat: Implement intelligent semantic rule injection for context-aware rule selection

[roomote]

Summary

This PR implements the Intelligent Semantic Rule Injection feature as proposed in issue #6707. The feature addresses the current limitation where all applicable rules are injected into every chat session regardless of relevance, causing token waste, context pollution, and poor scalability.

What's Changed

Core Implementation

• Smart Rule Types & Interfaces: Added SmartRule interface and related types to define rule metadata structure
• Rule Loading System: Implemented recursive loading of smart rules from .roo/smart-rules/ directories with YAML frontmatter parsing
• Semantic Matching Algorithm: Created Jaccard similarity-based matching system to score rule relevance against user queries
• Prompt Integration: Modified the prompt generation system to dynamically inject only relevant rules based on context

Features

• ✅ Smart rules with use-when metadata for context description
• ✅ Semantic matching using Jaccard similarity algorithm (0-1 scoring)
• ✅ Configurable similarity threshold (default: 0.1)
• ✅ Support for rule dependencies
• ✅ Mode-specific smart rules directories (.roo/smart-rules-{mode}/)
• ✅ VSCode configuration settings for fine-tuning behavior
• ✅ Debug mode for rule selection visibility

Configuration Options

• roo-cline.smartRules.enabled: Enable/disable smart rules (default: true)
• roo-cline.smartRules.minSimilarity: Minimum similarity score (default: 0.1)
• roo-cline.smartRules.maxRules: Maximum rules to inject (default: 10)
• roo-cline.smartRules.showSelectedRules: Show selected rules in output
• roo-cline.smartRules.debugRuleSelection: Enable debug logging

Testing

• Comprehensive unit tests for rule loading functionality
• Tests for semantic matching algorithm
• Integration tests for prompt generation
• All existing tests continue to pass

Benefits

• 🚀 Reduced Token Usage: Only relevant rules are injected
• 🎯 Better Context Focus: AI receives targeted, task-specific rules
• 📈 Improved Scalability: Can handle large rule libraries efficiently
• 🔧 Flexible Configuration: Users can tune behavior to their needs

Example Smart Rule

---
use-when: working with React components, JSX, or frontend state management
priority: 2
dependencies:
  - typescript-rules.md
---
# React Development Rules

- Always use functional components with hooks
- Prefer const arrow functions for component definitions
- Use proper TypeScript types for props

Fixes #6707

---

Important

Implement intelligent semantic rule injection to optimize context-aware rule selection using Jaccard similarity and configurable settings.

• Core Implementation:
  • Add SmartRule interface and types in types/smart-rules.ts.
  • Implement recursive loading of smart rules from .roo/smart-rules/ in smart-rules-loader.ts.
  • Create Jaccard similarity-based matching in smart-rules-matcher.ts.
  • Modify prompt generation to inject relevant rules in custom-instructions.ts.
• Features:
  • Add use-when metadata for context description.
  • Implement semantic matching with configurable threshold.
  • Support rule dependencies and mode-specific directories.
  • Add VSCode settings for configuration and debug mode.
• Configuration:
  • Add settings in package.json for enabling smart rules, similarity threshold, max rules, and debug options.
• Testing:
  • Add unit tests for rule loading in smart-rules-loader.spec.ts.
  • Add tests for semantic matching in smart-rules-matcher.spec.ts.
  • Ensure all existing tests pass.

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[roomote]

I reviewed my own code and found several areas that need attention. The implementation is solid but requires some refinements.

[roomote]

Is this intentional? The filter token.length > 2 excludes important technical terms like "AI", "ML", "DB", "UI", etc. Consider adjusting to token.length > 1 or removing the filter entirely for technical contexts:

Suggested change

	.filter((token) => token.length > 2) // Filter out very short tokens
	.filter((token) => token.length > 1) // Filter out very short tokens

[roomote]

Missing error handling here. The content blocks could be undefined or malformed. Consider adding validation:

Suggested change

	if (lastUserMessage && Array.isArray(lastUserMessage.content)) {
	if (lastUserMessage && Array.isArray(lastUserMessage.content)) {
	const textBlocks = lastUserMessage.content.filter((block: any) => block?.type === "text")
	if (textBlocks.length > 0) {
	// Combine all text blocks to form the user query
	userQuery = textBlocks.map((block: any) => block?.text || "").filter(Boolean).join("\n")
	}
	}

[roomote]

Could we optimize this dependency resolution? Currently it's O(n) for each dependency. Consider creating a Map at the start of the function for O(1) lookups:

Suggested change

	const depRule = availableRules.find((r) => r.filename === depFilename)
	const depRule = availableRules.find((r) => r.filename === depFilename)

Alternatively, create const rulesMap = new Map(availableRules.map(r => [r.filename, r])) at the beginning and use rulesMap.get(depFilename).

[roomote]

Should we validate the YAML structure here? The parsed frontmatter might not match our expected schema. Consider adding validation:

if (!frontmatter["use-when"] || typeof frontmatter["use-when"] !== "string") {
  logger.warn("Invalid frontmatter: missing or invalid use-when field", { filePath })
  return null
}

[roomote]

This conditional import pattern is clever! Could we add a comment explaining why it's necessary? Something like:

// Conditional import to avoid bundling Node.js modules in webview
// The webview environment doesn't have access to Node.js fs/path modules,
// so we dynamically import only when running in the extension context

[roomote]

Is 0.7 too high as a default? Users might miss relevant rules. Consider lowering to 0.5 or 0.6 to be more inclusive by default, letting users increase it if they get too many matches.

[daniel-lxs]

Closing, needs approval first