fix: md linting

amburi · amburi · commit 4efaf04f4dc2 · 2025-08-29T14:11:24.000+02:00
diff --git a/patterns/1-initial/ai-code-generation-context.md b/patterns/1-initial/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 +42,64 @@ 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
 
@@ -101,33 +108,35 @@ Create a `innersource-ai/` folder in the repository root containing:
 ### Usage Patterns
 
 #### For Contributors
-* **Before Starting**: Review the AI context package to understand project standards
-* **During Development**: Reference specific files when prompting AI tools
-* **Code Generation**: Include relevant context snippets in AI prompts
-* **Validation**: Use provided examples to verify AI-generated code aligns with standards
+
+ - **Before Starting**: Review the AI context package to understand project standards
+ - **During Development**: Reference specific files when prompting AI tools
+ - **Code Generation**: Include relevant context snippets in AI prompts
+ - **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
 
@@ -139,18 +148,18 @@ This solution balances the productivity benefits of AI tools with the quality re
 
 ## 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](../2-structured/base-documentation.md) - Provides the foundation documentation that AI context builds upon
-*  [InnerSource Portal](../2-structured/innersource-portal.md) -  Could include AI context availability as searchable metadata for project discovery
-*  [Trusted Committer](../2-structured/trusted-committer.md) -  Trusted Committers are responsible for maintaining and evolving the AI context package
+ - [Standard Base Documentation](../2-structured/base-documentation.md): Provides the foundation documentation that AI context builds upon
+ - [InnerSource Portal](../2-structured/innersource-portal.md): Could include AI context availability as searchable metadata for project discovery
+ - [Trusted Committer](../2-structured/trusted-committer.md): Trusted Committers are responsible for maintaining and evolving the AI context package
 
 ## License
 
