mikkihugo/claude-code-zen

AI Summary: Address 3,672 ESLint warnings in the Claude-Zen project, primarily focusing on reducing the use of 'any' types and improving overall code quality. This involves gradually replacing 'any' types with specific type definitions, implementing stricter TypeScript settings, and documenting remaining 'any' type usage with justifications. The goal is to reduce 'any' type usage by at least 50% and prevent new instances in future code.

AI Summary: Implement integration tests for a multi-crate system focusing on data pipelines, model registries, and CLI interactions. Tests should cover end-to-end workflows, cross-crate communication, error handling, performance, and real-world scenarios. The goal is to ensure seamless interaction between different components of the system.

AI Summary: Implement property-based testing using proptest in Rust to ensure numerical stability, validate invariants, and test edge cases within a neural network codebase. This involves testing various aspects like gradient calculations, activation functions, loss functions, matrix operations, model invariants (output dimensions, parameter bounds, state consistency), and data processing (scaling, window generation, missing value handling). Custom generators for time series data, neural network inputs, and configurations will likely be needed.

AI Summary: Implement comprehensive test coverage (unit, integration, property-based, performance, and documentation tests) for a Rust neural network codebase, aiming for >80% coverage and integrating tests into the CI/CD pipeline. This involves testing core functionality, integration with external libraries (ruv-FANN), time series processing, error handling, and concurrency.

AI Summary: Implement comprehensive performance benchmarks for neural network operations within a Rust project, covering model operations, training, data processing, memory usage, and scaling. This includes establishing baselines, enabling regression detection in CI, generating performance reports, and identifying optimization opportunities. The benchmarks should compare against existing baselines (e.g., ruv-FANN) and explore different model architectures and configurations.

AI Summary: Implement comprehensive unit tests for the `neuro-divergent-models` crate, covering various neural network models (MLP, RNN, LSTM, GRU, Transformers, etc.). Tests should cover model construction, forward/backward passes, training, and integration with the `ruv-FANN` library. Success is measured by complete test coverage and validation of model behavior.

AI Summary: Implement comprehensive unit tests for the `neuro-divergent-core` crate, covering traits, data structures, error handling, configuration, and integration layers. The goal is to achieve >90% code coverage and ensure all public APIs are thoroughly tested, including edge cases. This task is part of a larger project (#229) focused on a code-first orchestration layer.

AI Summary: Integrate the existing 5-phase SPARC (Specification → Pseudocode → Architecture → Refinement → Completion) development methodology into the Claude-Zen CLI. This involves creating CLI commands to manage the SPARC workflow, connecting it to the existing document-driven system and template library, enhancing the template system for easier selection and custom creation, implementing progress tracking and validation, and ensuring seamless integration with neural auto-discovery and swarm coordination features. The goal is to enable a complete, automated, and validated development workflow from initial requirements to final code implementation.

AI Summary: Integrate five database adapters (Kuzu, LanceDB, PostgreSQL, MySQL, SQLite) into a system, including CLI commands and REST API endpoints. This involves testing the adapters, implementing a database provider factory, designing multi-database workflows, and ensuring proper health monitoring and connection pooling.

AI Summary: Integrate a 665-line neural auto-discovery system into the `claude-zen` CLI by adding command routing in `src/interfaces/terminal/main.ts` and `bin/claude-zen`, exporting the command in `src/interfaces/cli/commands/index.ts`, validating existing dependencies and their integration with the auto-discovery system, and testing the interactive TUI interface. The goal is to enable users to discover projects, analyze their codebases, and optionally create swarms using the `claude-zen discover` command with various options.

AI Summary: Resolve 2,544 TypeScript compilation errors across multiple project domains. The strategy involves prioritizing domains with high error counts and interdependencies, focusing on root cause fixes rather than workarounds. Success is measured by a clean build, passing tests, no functional regressions, and a clean CI/CD pipeline.

AI Summary: Integrate the SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology into the existing Claude-Zen project. This involves updating the project's documentation to reflect the new methodology, improving the README and installation instructions for clarity and ease of use, and potentially re-implementing parts of the project to align with the SPARC workflow. The goal is to enhance the systematic development process within the Claude-Zen framework.

AI Summary: Enhance the command-line interface (CLI) of the Claude-Zen project with project management commands and update the version to 1.0.28. This involves adding new CLI functionalities for managing projects, improving the user experience, and potentially updating documentation to reflect these changes. The work should align with the existing document-driven development workflow outlined in the project README.

AI Summary: Re-implement the Ultrasonic Agentics feature, addressing merge conflicts encountered in the original implementation. This involves reviewing the existing code (branch: Ultrasonic-Agentics), resolving conflicts, and ensuring the feature functions correctly within the larger Claude-Zen system. The project uses a document-driven development workflow, so relevant documentation (likely within the `05-features/` and `06-tasks/` directories) should be consulted and updated.

AI Summary: Re-implement dual runtime support for the Claude-Zen project, ensuring compatibility with multiple runtime environments. This involves improving Jest mocking, fixing priority scheduling, adding health status monitoring, and ensuring Mock API compatibility. The implementation should follow the project's document-driven development workflow, starting with task creation in the `06-tasks/` directory and progressing through the code development and validation stages.