fix: liniting issue and add known instances

Author: amburi

patterns/2-structured/ai-code-generation-context.md

@@ -12,24 +12,24 @@ With the growing use of AI tools (like GitHub Copilot, ChatGPT, or custom LLMs),
 
 ## Context
 
-- InnerSource adoption is in place across the organization
-- Developers are encouraged to use AI tools to improve productivity
-- Contributors may not be familiar or failed to prompt meticulously while generating code through AI with the target codebase's idioms, architecture, or constraints
-- Maintainers want to empower contributors using AI while preserving code consistency and maintainability
-- Multiple teams are contributing to shared repositories with varying levels of familiarity with project standards
-- Code review processes are becoming bottlenecked by AI-generated code that requires significant rework
+ - InnerSource adoption is in place across the organization
+ - Developers are encouraged to use AI tools to improve productivity
+ - Contributors may not be familiar or failed to prompt meticulously while generating code through AI with the target codebase's idioms, architecture, or constraints
+ - Maintainers want to empower contributors using AI while preserving code consistency and maintainability
+ - Multiple teams are contributing to shared repositories with varying levels of familiarity with project standards
+ - Code review processes are becoming bottlenecked by AI-generated code that requires significant rework
 
 ## Forces
 
-- **AI Model Limitations**: AI models generate code based on generalized training data, not project-specific patterns, leading to generic solutions that may not fit the project's architecture
-- **Knowledge Gap**: New contributors using AI tools might unknowingly bypass existing architectural norms, coding standards, and established patterns
-- **Review Overhead**: AI-assisted PRs can significantly increase review load if not aligned with existing practices, as maintainers must spend time explaining and correcting deviations
-- **Productivity vs. Quality Trade-off**: While AI tools boost individual productivity, they can reduce overall team productivity if the generated code requires extensive rework
-- **Context Switching Cost**: Developers benefit from AI tools only when they have the right contextual grounding, but manually providing this context for each AI interaction is time-consuming
-- **Inconsistent Standards**: Different AI tools and different prompting approaches by contributors can lead to wildly inconsistent code styles and patterns
-- **Maintenance Burden**: Creating and maintaining comprehensive AI context requires ongoing effort from maintainers
-- **Tool Integration Complexity**: Different AI tools have different ways of consuming context, making it challenging to create universal guidance
-- **AI Tool Cost Constraints**: Comprehensive AI context increases processing costs (AI tools charge based on "tokens" - units of text measurement) and usage limits, requiring strategic balance between context completeness and efficiency
+ - **AI Model Limitations**: AI models generate code based on generalized training data, not project-specific patterns, leading to generic solutions that may not fit the project's architecture
+ - **Knowledge Gap**: New contributors using AI tools might unknowingly bypass existing architectural norms, coding standards, and established patterns
+ - **Review Overhead**: AI-assisted PRs can significantly increase review load if not aligned with existing practices, as maintainers must spend time explaining and correcting deviations
+ - **Productivity vs. Quality Trade-off**: While AI tools boost individual productivity, they can reduce overall team productivity if the generated code requires extensive rework
+ - **Context Switching Cost**: Developers benefit from AI tools only when they have the right contextual grounding, but manually providing this context for each AI interaction is time-consuming
+ - **Inconsistent Standards**: Different AI tools and different prompting approaches by contributors can lead to wildly inconsistent code styles and patterns
+ - **Maintenance Burden**: Creating and maintaining comprehensive AI context requires ongoing effort from maintainers
+ - **Tool Integration Complexity**: Different AI tools have different ways of consuming context, making it challenging to create universal guidance
+ - **AI Tool Cost Constraints**: Comprehensive AI context increases processing costs (AI tools charge based on "tokens"  - units of text measurement) and usage limits, requiring strategic balance between context completeness and efficiency
 
 ## Solution
 
@@ -42,57 +42,57 @@ Create a `innersource-ai/` folder in the repository root containing:
 #### Core Documentation Files (Required)
 
 `PROMPT.md`: Project-specific instructions for AI tools
-- Naming conventions (variables, functions, classes, files)
-- Logging patterns and error handling approaches
-- Testing strategy and preferred testing frameworks
-- Code formatting and style preferences
-- Common anti-patterns to avoid
-- Preferred libraries and frameworks for specific tasks
+ - Naming conventions (variables, functions, classes, files)
+ - Logging patterns and error handling approaches
+ - Testing strategy and preferred testing frameworks
+ - Code formatting and style preferences
+ - Common anti-patterns to avoid
+ - Preferred libraries and frameworks for specific tasks
 
 `ARCHITECTURE.md`: Lightweight system overview
-- High-level system architecture and component relationships
-- Data flow patterns and API design principles
-- Dependency management guidelines
-- Module organization and layering principles
-- Integration patterns with external systems
+ - High-level system architecture and component relationships
+ - Data flow patterns and API design principles
+ - Dependency management guidelines
+ - Module organization and layering principles
+ - Integration patterns with external systems
 
 `STYLE_GUIDE.md`: Comprehensive coding guidelines
-- Language-specific style rules
-- Code organization patterns
-- Documentation standards
-- Performance considerations
-- Security guidelines and common vulnerabilities to avoid
+ - Language-specific style rules
+ - Code organization patterns
+ - Documentation standards
+ - Performance considerations
+ - Security guidelines and common vulnerabilities to avoid
 
 #### Optional Enhancements (Implement As Needed)
 
 ##### Practical Examples
 
 `EXAMPLES/`: Sample code files demonstrating best practices
-- `good-examples/`: Well-written code snippets with explanations
-- `bad-examples/`: Common mistakes with explanations of why they're problematic
-- `refactoring-examples/`: Before/after code showing proper improvements
-- Template files for common patterns (controllers, services, utilities)
+ - `good-examples/`: Well-written code snippets with explanations
+ - `bad-examples/`: Common mistakes with explanations of why they're problematic
+ - `refactoring-examples/`: Before/after code showing proper improvements
+ - Template files for common patterns (controllers, services, utilities)
 
 ##### Configuration and Tooling
 
 `CONFIG/`: Shared formatter and analysis configurations
-- `.prettierrc`, `.eslintrc`, or equivalent formatting configs
-- `.editorconfig` for consistent editor settings
-- Static analysis tool configurations
-- Pre-commit hook configurations
+ - `.prettierrc`, `.eslintrc`, or equivalent formatting configs
+ - `.editorconfig` for consistent editor settings
+ - Static analysis tool configurations
+ - Pre-commit hook configurations
 
 `INTEGRATION/`: AI tool-specific integration guides
-- GitHub Copilot workspace configuration
-- Custom GPT instructions and prompts
-- IDE plugin configurations
-- CLI tool integration examples
+ - GitHub Copilot workspace configuration
+ - Custom GPT instructions and prompts
+ - IDE plugin configurations
+ - CLI tool integration examples
 
 ##### Advanced Features
 
 `EMBEDDINGS/` (Optional): For advanced LLM integrations
-- Searchable knowledge base of project patterns
-- Vector embeddings of code examples
-- Semantic search capabilities for finding relevant patterns
+ - Searchable knowledge base of project patterns
+ - Vector embeddings of code examples
+ - Semantic search capabilities for finding relevant patterns
 
 ### Implementation Strategy
 
@@ -107,27 +107,27 @@ Create a `innersource-ai/` folder in the repository root containing:
 4. **Validation**: Use provided examples to verify AI-generated code aligns with standards
 
 #### For AI Tool Integration
-- **Direct Reference**: Copy relevant sections into AI tool prompts
-- **URL Integration**: Reference files via repository URLs in AI tools that support web access
-- **IDE Integration**: Configure AI plugins to automatically include context
-- **Custom Workflows**: Integrate context into CI/CD pipelines for automated validation
+ - **Direct Reference**: Copy relevant sections into AI tool prompts
+ - **URL Integration**: Reference files via repository URLs in AI tools that support web access
+ - **IDE Integration**: Configure AI plugins to automatically include context
+ - **Custom Workflows**: Integrate context into CI/CD pipelines for automated validation
 
 ### Maintenance Strategy
 
-- **Version Control**: Track changes to AI context alongside code changes
-- **Regular Updates**: Review and update context as project standards evolve
-- **Community Contribution**: Allow contributors to suggest improvements to AI context
-- **Metrics Tracking**: Monitor the effectiveness of AI context through code review metrics
+ - **Version Control**: Track changes to AI context alongside code changes
+ - **Regular Updates**: Review and update context as project standards evolve
+ - **Community Contribution**: Allow contributors to suggest improvements to AI context
+ - **Metrics Tracking**: Monitor the effectiveness of AI context through code review metrics
 
 ## Resulting Context
 
-- **Improved Code Quality**: AI-assisted contributions become consistent with existing code standards and architectural patterns from the first submission
-- **Reduced Review Friction**: Maintainers can trust incoming PRs more readily, significantly reducing review fatigue and time-to-merge
-- **Enhanced Contributor Experience**: Contributors using AI produce better, more maintainable code even on their first attempts, leading to increased confidence and participation
-- **Scalable Collaboration**: Opens the door to scalable, AI-aware InnerSource collaboration across teams without sacrificing code quality
-- **Knowledge Preservation**: Project knowledge becomes more explicit and accessible, reducing dependency on tribal knowledge
-- **Faster Onboarding**: New contributors can leverage AI tools effectively from day one, reducing the learning curve for project-specific patterns
-- **Consistent Evolution**: As AI tools improve, the context package ensures that enhanced capabilities are channeled toward project-appropriate solutions
+ - **Improved Code Quality**: AI-assisted contributions become consistent with existing code standards and architectural patterns from the first submission
+ - **Reduced Review Friction**: Maintainers can trust incoming PRs more readily, significantly reducing review fatigue and time-to-merge
+ - **Enhanced Contributor Experience**: Contributors using AI produce better, more maintainable code even on their first attempts, leading to increased confidence and participation
+ - **Scalable Collaboration**: Opens the door to scalable, AI-aware InnerSource collaboration across teams without sacrificing code quality
+ - **Knowledge Preservation**: Project knowledge becomes more explicit and accessible, reducing dependency on tribal knowledge
+ - **Faster Onboarding**: New contributors can leverage AI tools effectively from day one, reducing the learning curve for project-specific patterns
+ - **Consistent Evolution**: As AI tools improve, the context package ensures that enhanced capabilities are channeled toward project-appropriate solutions
 
 ## Rationale
 
@@ -137,20 +137,24 @@ The `innersource-ai/` folder approach is intentionally explicit and discoverable
 
 This solution balances the productivity benefits of AI tools with the quality requirements of professional software development, creating a sustainable approach to AI-assisted InnerSource collaboration.
 
+## Known Instances
+
+To be added.
+
 ## Status
 
-- Structured
-- Drafted in August 2025
+ - Structured
+ - Drafted in August 2025
 
 ## Author
 
 [Amburi Roy](https://www.linkedin.com/in/amburi/)
 
 ## Related Patterns
 
-- [Standard Base Documentation](../base-documentation.md) - Provides the foundation documentation that AI context builds upon
-- [InnerSource Portal](../innersource-portal.md) - Could include AI context availability as searchable metadata for project discovery
-- [Trusted Committer](../trusted-committer.md) - Trusted Committers are responsible for maintaining and evolving the AI context package
+ - [Standard Base Documentation](../base-documentation.md) - Provides the foundation documentation that AI context builds upon
+ - [InnerSource Portal](../innersource-portal.md) - Could include AI context availability as searchable metadata for project discovery
+ - [Trusted Committer](../trusted-committer.md) - Trusted Committers are responsible for maintaining and evolving the AI context package
 
 ## License