*Follow the rules of `.cursorrules` file. This memories file serves as a chronological log of all project activities, decisions, and interactions. Use "mems" trigger word for manual updates during discussions, planning, and inquiries. Development activities are automatically logged with clear descriptions, and #tags for features, bugs, and improvements. Keep entries in single comprehensive lines under "### Interactions" section. Create @memories2.md when reaching 1000 lines.*

# Project Memories (AI & User) 🧠

### Interactions

Implemented strict schema enforcement in llm_ci_runner.py using Semantic Kernel's KernelBaseModel, supporting dynamic Pydantic models from JSON schemas, robust response extraction, token-level validation, and backward compatibility. Achieved high production readiness and comprehensive constraint checking. #schema-enforcement #semantic-kernel  
   
Refactored schema-to-Pydantic conversion using json-schema-to-pydantic library, eliminated manual type mapping, reduced code by 150+ lines, maintained KernelBaseModel approach—improved robustness and maintainability. #refactoring #pydantic  
   
Built full test infrastructure: 69/69 unit tests passing, realistic API mocks, systematic test failure resolution, Given-When-Then pattern adoption, covering all major functions and integration cases, with acceptance tests (LLM-as-judge pattern) and improved documentation. #testing #unit-tests #integration-tests  
   
Transitioned to pytest framework with Rich formatting, restructured acceptance tests into parametrized classes and fixtures, enabled extensibility and professional reporting—consistent, maintainable acceptance tests with full coverage. #pytest #acceptance-testing  
   
Established comprehensive GitHub CI/CD pipeline: parallel lint/type-check/test/security jobs using uv, JUnit reporting, secret detection, pip-audit integration, and 100% test coverage enforcement. #ci-cd #github-actions  
   
Rewrote README.md as "AI-First DevOps Toolkit," shifting narrative to AI-First DevOps transformation, contextualizing features for autonomous development and continuous AI quality gates. Integrated blog article for strategy alignment. #readme #ai-first-devops  
   
Rewrote examples/uv-usage-example.md with real CI/CD scenarios (PR automation, security analysis, changelog generation, code review, AI pipelines), comprehensive schema files, best practices, and streamlined README organization. #examples #documentation  
   
Enhanced llm_ci_runner.py with Rich pretty printing of LLM responses—colored panels for both text and structured outputs, improving console UX and aligning with acceptance test report style. #rich-formatting  
   
Reorganized examples/ into logical subfolders (basic, devops, security, ai-first), each with input, schema, and docs; added complex "autonomous development plan" example. Improved documentation and example discoverability. #examples #organization  
   
Fixed example path references after reorg, preserving backward compatibility; validated all example schemas, ensured clear separation of simple vs comprehensive examples. #examples #bug-fix  
   
Developed generic example test framework: auto-discovery, schema validation per example type, pytest/standalone modes, extensible patterns, supporting rapid addition and reliable validation of new examples. #testing #extensibility  
   
Refactored to dynamic pytest-based example discovery; auto-tests generated for each example, including schema and enum/constraint validation, eliminating manual test maintenance and ensuring scalability. #testing #auto-discovery  
   
Corrected examples/README.md to match actual folders, maintaining documentation/code consistency. #documentation  
   
Created "vibe-coding-workflow" AI-First example with Jinja-like schema, documenting autonomous workflow creation, quality gates, and AI integration; finalized ai-first examples set. #vibe-coding #ai-first-devops  
   
Acceptance tests switched to convention-based auto-discovery for all examples (no hardcoded files), improving test extensibility and maintainability. #acceptance-testing  
   
Introduced smoke test mode to acceptance tests (fast, LLM-execution free), implemented deprecation stubs for obsolete test files. Ensured single test source of truth for all examples. #smoke-testing #test-strategy  
   
Optimized test execution: merged reliability, schema, and (conditional) LLM-as-judge quality checks into single call per example, cutting LLM invocations by 42% and halving test times without sacrificing coverage. #cost-optimization #test-efficiency  
   
Wrote comprehensive acceptance/README.md covering LLM-as-judge testing, smoke/testing modes, quality gates, DevOps integration, performance/cost gains, and extension patterns. #documentation #llm-as-judge  
   
Refined README features list: technical focus on schema enforcement, retry, rich logging, CLI, acceptance framework; outlined upcoming metrics features; reduced marketing language. #documentation  
   
Implemented Jinja2 template support in llm_ci_runner.py: auto-detects .jinja/.j2 files, loads and renders using Semantic Kernel/Jinja2PromptTemplate, supports advanced template logic, maintains backward compatibility, and ships with comprehensive example and updated tests. #jinja2 #template-engine  
   
Extended acceptance tests to auto-discover YAML/Handlebars template examples, improved template execution and schema evaluation, ensuring all template-based examples are properly tested and validated. #templates #acceptance-testing  
   
Enhanced acceptance test auto-discovery to support Jinja2 templates (.jinja/.j2 files) in addition to Handlebars templates (.hbs files): updated llm_ci_runner fixture to handle multiple template formats, expanded pytest_generate_tests to discover all template types generically, added Jinja2-specific evaluation criteria, and made template type detection dynamic for future extensibility. #jinja2 #acceptance-testing #auto-discovery  

*Note: This memory file maintains chronological order and uses tags for better organization. Cross-reference with @memories2.md will be created when reaching 1000 lines.*
