- Convert all markdown files from Chinese to English
- Remove all emoji/icon decorations (🔧📋⚙️✅🏁🔍📚⛔⭐)
- Update all section headers, descriptions, and documentation
- Keep all content logic, structure, code examples unchanged
- Maintain template variables and file paths as-is
Files converted (9 files total):
- SKILL.md: Output structure comments
- templates/skill-md.md: All Chinese descriptions and comments
- specs/reference-docs-spec.md: All section headers and explanations
- phases/01-requirements-discovery.md through 05-validation.md (5 files)
- specs/execution-modes.md, skill-requirements.md, cli-integration.md, scripting-integration.md (4 files)
- templates/sequential-phase.md, autonomous-orchestrator.md, autonomous-action.md, code-analysis-action.md, llm-action.md, script-template.md (6 files)
All 16 files in skill-generator are now fully in English.
- Create specs/reference-docs-spec.md with comprehensive guidelines for phase-based reference document organization
- Update skill-generator's Mandatory Prerequisites to include new reference-docs-spec
- Refactor skill-md.md template to generate phase-based reference tables with 'When to Use' guidance
- Add generateReferenceTable() function to automatically create structured reference sections
- Replace flat template reference lists with phase-based navigation
- Update skill-generator's own SKILL.md to demonstrate correct reference documentation pattern
- Ensure all generated skills will have clear document usage timing and context
- Updated AGENTS.md to include CLI tools usage and configuration details.
- Introduced a new script template for both Bash and Python, outlining usage context, calling conventions, and implementation guidelines.
- Provided examples for common patterns in both Bash and Python scripts.
- Established a directory convention for script organization and naming.
- Updated Phase 1 and Phase 2 documentation to include next phase links and data flow details.
- Expanded Phase 5 documentation to include comprehensive validation and README generation steps, along with validation report structure.
- Added purpose and usage context sections to various action and script templates (e.g., autonomous-action, llm-action, script-bash).
- Improved commands management by simplifying the command scanning logic and enabling/disabling commands through renaming files.
- Enhanced dashboard command manager to format group names and display nested groups with appropriate icons and colors.
- Updated LiteLLM executor to allow model overrides during execution.
- Added action reference guide and template reference sections to the skill-tuning SKILL.md for better navigation and understanding.
- Deleted security requirements specification file to streamline documentation.
- Removed best practice finding template to enhance clarity and focus on critical issues.
- Eliminated report template and security finding template to reduce redundancy and improve maintainability.
- Updated skill generator templates to enforce mandatory prerequisites for better compliance with design standards.
- Bumped package version from 6.3.18 to 6.3.23 for dependency updates.
- 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.
- 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.
