*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

Fixed critical Docker build failures by resolving package structure issues: 1) **Package Location**: Updated pyproject.toml to specify `[tool.hatch.build.targets.wheel] packages = ["llm_ci_runner"]` so Hatchling can find the package, 2) **Directory Structure**: Fixed Dockerfile COPY command from `COPY llm_ci_runner/ README.md LICENSE NOTICE ./` to `COPY llm_ci_runner/ ./llm_ci_runner/` to preserve package structure, 3) **Python Path**: Added `ENV PYTHONPATH="/app"` to ensure Python can find the module, 4) **Environment Variables**: Added missing `ENV UV_COMPILE_BYTECODE=1` in runtime stage, 5) **Goss Tests**: Updated goss.yaml to match actual help text "LLM CI Runner - AI-powered automation for CI/CD pipelines". All 46 goss tests now passing (100% success). Key insight: Docker package copying must preserve directory structure for Python modules to be importable. #docker #package-structure #goss-tests

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  
   
Created generic, abstract LLM-as-judge evaluation approach: implemented generic_llm_judge fixture that can evaluate any example based on input, schema, and output without hard-coupled criteria, added TestGenericExampleEvaluation class demonstrating completely abstract evaluation, generated criteria dynamically based on example characteristics (template vs JSON, schema presence, name patterns), eliminated need for specific evaluation logic per example type, and made system extensible for new example types without code changes. #generic-evaluation #abstract-testing #llm-as-judge  
   
Cleaned up acceptance tests by removing hard-coupled TestExampleComprehensive class: replaced with generic TestGenericExampleEvaluation class that covers all example types without specific criteria, kept TestCustomScenarios for extensibility demonstration, eliminated 200+ lines of hard-coupled evaluation logic, and maintained same functionality with abstract, generic approach. #test-cleanup #generic-approach #maintainability  

*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.*
