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Found 30 Skills
Review a single file or all files in a folder for data inconsistencies, reference errors, typos, and unclear terminology using parallel sub-agents
Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.
Delegate tasks to the cost-effective opencode/glm-5 model. Use when you need inexpensive task execution, simple research, or delegating work that doesn't require the most powerful models.
Invoke parallel document-specialist agents for external web searches and documentation lookup
Autonomous project gardening by a coordinated team of agents. Spawns a team of gardeners that each run the `garden` skill in parallel, coordinating via a shared task list to avoid duplicate work. Use when the user wants to tend multiple small issues in one pass. Invoke with /gardeners.
Illustre automatiquement le journal d'une aventure BFRPG en générant des images pour les moments clés (combats, explorations, découvertes). Utilise la génération parallèle pour une performance optimale.
Invokes Google Gemini models for structured outputs, multi-modal tasks, and Google-specific features. Use when users request Gemini, structured JSON output, Google API integration, or cost-effective parallel processing.
Cognitive science-based deep source code understanding assistant (Chinese improved version). Supports three analysis modes: Quick (overview), Standard (comprehension), Deep (mastery, automatically uses parallel processing for large projects). Integrates elaborative interrogation, self-explanation testing, and retrieval practice to help truly understand and master code.
Resolve all PR comments using parallel processing. Use when addressing PR review feedback, resolving review threads, or batch-fixing PR comments.
Resolve all pending CLI todos using parallel processing, compound on lessons learned, then clean up completed todos.
Novel content polishing and optimization, suitable for user requests such as "Help me polish this novel", "Improve the writing style", "Optimize chapter rhythm", "Enhance this highlight", "Make dialogues more natural", "Make this passage more engaging", "Optimize novel writing style", "Adjust chapter rhythm", "Make dialogues more realistic", "Help me revise this content", "Polish novel", "Optimize highlights", "Improve writing style", "Make this passage more immersive", etc. It provides 3 levels of polishing, focusing on optimization of writing style and content, supporting special optimizations such as style adaptation, rhythm tightening, highlight enhancement, dialogue optimization, etc. **Polished results directly modify the chapters/ directory, and automatic backups are made to .sumeru/write/original/ before modification**. **Sub-Agents are used for parallel processing during batch polishing, with each Agent responsible for a maximum of 3 chapters**
Fan out a prompt to multiple AI coding agents in parallel and synthesize their responses.