This page documents suggestions for additional AI-assisted tools that could enhance wiki maintenance for Remixed Dungeon.

The wiki currently benefits from several AI-assisted tools that automate various maintenance tasks:

• find_red_links.py: Identifies broken links and missing pages, with the ability to find similar pages to missing links using fuzzy string matching
• wiki_potential_links_and_duplicates.py: Identifies potential links between pages based on semantic similarity and detects potential duplicate content
• dokuwiki_linter.py: Validates DokuWiki pages against established standards and naming conventions
• check_mr_links.py: Verifies that all file references in mr namespace pages point to actual existing files
• check_language_links.py: Ensures internal links on a page are in the same language as the page itself
• wiki_page_rename.py: Renames wiki pages and updates all internal links to the renamed page
• fix_wiki_redirects.py: Automatically fixes redirect patterns by updating links to follow wiki's naming conventions

The following tools are suggested for future development to further enhance wiki maintenance:

A tool that automatically generates initial content for new wiki pages based on information extracted from the game code:

• Purpose: Automatically generate initial page content for new mobs, items, spells, etc. based on source code analysis
• Implementation: Extract class definitions, methods, stats, and descriptions from Java, Lua, and JSON files to create a basic page structure
• Benefits: Reduce time needed to create new pages and ensure accuracy from the start
• Input: Java class files, Lua script files, JSON configuration files
• Output: Basic DokuWiki page with stats, mechanics, and source code references

A tool that helps maintain consistency across different language versions of the same page:

• Purpose: Identify when content differs significantly between language versions and suggest updates
• Implementation: Compare structure and content of equivalent pages in different languages
• Benefits: Maintain consistency across localized versions of the wiki
• Features: Highlight discrepancies, suggest translations for new content, flag pages that might need updating

A tool that analyzes wiki content and suggests improvements based on completeness and accuracy:

• Purpose: Identify pages that might be missing important information or have outdated content
• Implementation: Compare wiki content against latest game code to identify discrepancies
• Benefits: Keep wiki content up-to-date with game changes and ensure completeness
• Features: Compare stats between wiki and code, identify missing content based on code features, flag outdated information

A tool that analyzes relationships between different game entities and suggests relevant links:

• Purpose: Identify implicit relationships between game entities that should be linked in the wiki
• Implementation: Analyze code for connections between mobs, items, spells, etc. and suggest appropriate links
• Benefits: Improve navigation and discovery of related content
• Features: Generate potential links, identify missing cross-references, suggest “see also” sections

A tool that suggests where images should be added or updated based on content analysis:

• Purpose: Identify pages that could benefit from visual content
• Implementation: Analyze text for references to visual elements and suggest appropriate images
• Benefits: Improve visual completeness of wiki pages
• Features: Suggest relevant images from the game assets, identify missing sprites, recommend image placement

A tool that evaluates page quality and completeness using AI:

• Purpose: Assess the quality and completeness of wiki pages
• Implementation: Analyze content depth, completeness against game mechanics, and adherence to standards
• Benefits: Identify pages that need improvement and prioritize maintenance efforts
• Features: Quality scoring system, completeness assessment, adherence to standards check

When developing new AI tools for wiki maintenance:

• Prioritize automation: Focus on tasks that are repetitive or time-consuming for human editors
• Maintain accuracy: Ensure AI suggestions are verified against source code before being implemented
• Preserve human oversight: AI tools should suggest or assist rather than fully automate critical decisions
• Follow existing patterns: New tools should integrate well with existing wiki maintenance workflows
• Consider multilingual support: Tools should work across all supported languages

• Code integration: New tools should be able to parse Java, Lua, JSON, and string resource files effectively
• DokuWiki syntax: Tools should properly generate and maintain DokuWiki formatting
• Git integration: Tools should work well with the git-based workflow of the wiki repository
• Performance: Tools should be efficient and not cause unnecessary delays in workflow
• User-friendliness: Tools should provide clear, actionable outputs for wiki maintainers