- Added a function to parse JSON streaming content in core-memory.js, extracting readable text from messages.
- Updated memory detail view to utilize the new parsing function for content and summary.
- Introduced an enableReview option in rules-manager.js, allowing users to toggle review functionality in rule creation.
- Simplified skill creation modal in skills-manager.js by removing generation type selection UI.
- Improved CLI executor to handle tool calls for file writing, ensuring proper output parsing.
- Adjusted CLI command tests to set timeout to 0 for immediate execution.
- Updated file watcher to implement a true debounce mechanism and added a pending queue status for UI updates.
- Enhanced watcher manager to handle queue changes and provide JSON output for better integration with TypeScript backend.
- Established TypeScript naming conventions documentation to standardize code style across the project.
- Introduced best practices requirements specification covering code quality, performance, maintainability, error handling, and documentation standards.
- Established quality standards with overall quality metrics and mandatory checks for security, code quality, performance, and maintainability.
- Created security requirements specification aligned with OWASP Top 10 and CWE Top 25, detailing checks and patterns for common vulnerabilities.
- Developed templates for documenting best practice findings, security findings, and generating reports, including structured markdown and JSON formats.
- Updated dependencies in the project, ensuring compatibility and stability.
- Added test files and README documentation for vector indexing tests.
- Updated Windows platform guidelines for path formats and Bash rules.
- Refactored CodexLens routes to improve GPU detection and indexing cancellation logic.
- Added FastEmbed installation status handling in the dashboard, including UI updates for installation and reinstallation options.
- Implemented local model management with improved API responses for downloaded models.
- Enhanced GPU selection logic in the model mode configuration.
- Improved error handling and user feedback for FastEmbed installation processes.
- Adjusted Python environment checks to avoid shell escaping issues on Windows.
- Implemented CLI commands for listing, downloading, deleting, and retrieving information about reranker models.
- Enhanced the dashboard UI to support embedding and reranker configurations with internationalization.
- Updated environment variable management for embedding and reranker settings.
- Added functionality to dynamically update model options based on selected backend.
- Improved user experience with status indicators and action buttons for model management.
- Integrated new reranker models with detailed metadata and recommendations.
- Implement comprehensive end-to-end tests for MCP Tool Execution, covering tool discovery, execution, parameter validation, error handling, and timeout scenarios.
- Introduce tests for the complete lifecycle of a workflow session, including initialization, task management, status updates, and archiving.
- Validate dual parameter format support and handle boundary conditions such as invalid JSON, non-existent sessions, and path traversal attempts.
- Ensure concurrent task updates are handled without data loss and that task data is preserved when archiving sessions.
- List sessions across all locations and verify metadata inclusion in the results.
- Added `rerankerModels` property to the `ProviderCredential` interface in `litellm-api-config.ts` to support additional reranker configurations.
- Introduced a numerically stable sigmoid function in `FastEmbedReranker` for score normalization.
- Updated the scoring logic in `FastEmbedReranker` to use raw float scores from the encoder and normalize them using the new sigmoid function.
- Adjusted the result mapping to maintain original document order while applying normalization.
Add --queues parameter to enable parallel queue formation with multiple
issue-queue-agents. Solutions are partitioned by file overlap to minimize
cross-queue conflicts. Queue groups are linked via queue_group field.
- Introduced a comprehensive code analysis action template for integrating code exploration and analysis capabilities.
- Added LLM action template for seamless integration of LLM calls with customizable prompts and tools.
- Implemented a benchmark search script to compare multiple search methods across various dimensions including speed, result quality, ranking stability, and coverage.
- Provided preset configurations for common analysis tasks and LLM actions, enhancing usability and flexibility.
- Add Command-to-Tool Mapping for pre_analysis commands
- Add Execution Mode Selection for implementation_approach steps
- command field exists → Execute CLI via Bash
- no command → Agent direct execution with test_commands
- Fix bash command syntax in execute.md for Windows compatibility
- Convert multi-line for loop to single-line format
- Use single quotes for grep patterns to avoid escaping issues
- Simplify agent prompt template in execute.md
- Path-based invocation instead of embedding task content
- Add [FLOW_CONTROL] and "Implement" markers to trigger agent behavior
- Enhance code-developer.md Task JSON parsing
- Add explicit Task JSON field mapping (requirements, acceptance, etc.)
- Add STEP 1/2/3 execution flow for clarity
- Add Pre-Analysis Execution format with command-to-tool mapping
- Define default behavior when command field is missing
- Priority: Use tech_stack from JSON before auto-detection
- Add execution_config alignment rules to action-planning-agent.md
- Ensure meta.execution_config matches implementation_approach
- "agent" mode: no command fields, cli_tool: null
- "hybrid" mode: some command fields
- "cli" mode: all steps have command fields
- Introduced a comprehensive template for autonomous actions, detailing structure, execution, and error handling.
- Added an orchestrator template to manage state and decision logic for autonomous actions.
- Created a sequential phase template to outline execution steps and objectives for structured workflows.
- Developed a skill documentation template to standardize the generation of skill entry files.
- Implemented a Python script to compare search results between hybrid and cascade methods, analyzing ranking changes.
- Update README.md/README_CN.md badges and version text to v6.3.18
- Update INSTALL.md version menu examples from v4.6.0 to v6.3.18
- Fix INSTALL_CN.md repository URL (Claude-CCW → Claude-Code-Workflow)
- Fix INSTALL_CN.md directory name (Dmsflow → Claude-Code-Workflow)
- Add normalizePath() for Windows case-insensitive comparison
- Add resolveMainRepoFromGitFile() to parse .git file when git command fails
- Enhanced fallback logic reads .git file content (gitdir: path) to find main repo
- Supports worktree detection even without git CLI available
getProjectRoot() now detects if running from a git worktree and
automatically resolves to the main repository path. This allows
all ccw issue commands to work correctly from within a worktree.
Uses git rev-parse --git-common-dir to find main repo's .git
