Loading...
Loading...
Found 77 Skills
Deep dive into a book. Collect information from six dimensions including chapter structure, background key points, problem impacts, solutions, term index, and further reading through parallel sub-agents, then output a Markdown deep learning note after cross-analysis. Trigger words: Analyze the book XX, Study XX, Reading notes for XX, book analysis.
Use this skill when managing cmux terminal panes, surfaces, and workspaces from Claude Code or any AI agent. Triggers on spawning split panes for sub-agents, sending commands to terminal surfaces, reading screen output, creating/closing workspaces, browser automation via cmux, and any task requiring multi-pane terminal orchestration. Also triggers on "cmux", "split pane", "new-pane", "read-screen", "send command to pane", or subagent-driven development requiring isolated terminal surfaces.
2-layer parallel agent hierarchy. Layer 1 deploys 3-50+ agents, each with independent context. Layer 2 adds 2+ sub-agents per member. No upper limit on either layer.
Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
Novel Logic/Plot Review, applicable to user requests such as "Help me check if there are bugs in my novel", "Check if there are timeline contradictions", "Check if characters are OOC", "Find plot conflicts between different parts", "Sort out whether foreshadowings are resolved", "Check the rationality of novel plots", "Check if there are plot loopholes", "Character behaviors are inconsistent with their personalities", "Check if the timeline is correct", "Find contradictions in the novel", "Help me sort out all foreshadowings", "Novel plot bug check", "Logical loophole troubleshooting", etc. It detects issues such as timeline conflicts, logical loopholes, character OOC, and missing foreshadowings. **Performs word count checks to ensure chapter word counts meet standards**, **generates a detailed issue list and automatically fixes all issues, with the fixed results directly modifying the chapters/ directory and automatically backing up the original files to .sumeru/write/original/ before modification**, **uses sub-Agents for parallel processing during batch review, with each Agent responsible for a maximum of 3 chapters**
Parallel DAG-plan implementation skill. Reads a v-planning plan directory (root.md + step-<n>.md files), topologically schedules ready steps, and fans them out as parallel sub-agents. Use whenever the user invokes /v-implement, points at a plan directory produced by /v-plan, or asks to "run the parallel plan", "implement the DAG", or "fan out the steps" — even without those exact words. For linear plans (single .md file), use `implementing` instead.
Vertical / parallel implementation planning skill. Creates DAG-structured plan directories where each step is an independent, QA-able vertical slice that sub-agents can pick up and implement in parallel. Use whenever the user wants a plan that fans out (multiple independent features), invokes /v-plan, or asks for a "parallel plan", "DAG plan", "vertical plan", or "plan that can be parallelized" — even if they don't say those exact words. Prefer the linear `planning` skill for strictly sequential work.
Hypothesis-driven deep research swarm. Spawns specialist sub-agents to investigate a task across codebase patterns, web sources, MCP tools, installed skills, and project dependencies — with evidence grading and adversarial challenge. Activates on: research, investigate, discover, deep research, how should I, what's the best way, explore options, analyze approaches, scout, prior art, feasibility.
This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of orchestrating context across multiple agents.
Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection, quality-focused prompting, and meta-judge → LLM-as-a-judge verification
Novel outline/worldview/character design, applicable to user requests such as "Help me write a novel outline", "Design the protagonist's character", "Create a worldview setting", "Build a novel plot framework", "Write volume-specific detailed outlines", "Design novel characters for me", "Create a fantasy worldview", "Help me sort out the novel plot", "Novel character setting", "Write chapter-by-chapter outlines for novels", "Plan the arrangement of cool points", "Create novel character cards", "Build a novel world", etc. It generates complete worldviews, character cards, plot outlines, and cool point plans, with automatic compliance checks to avoid infringement risks. **When generating a large number of chapter detailed outlines, sub-Agents are used for parallel processing, and each Agent is responsible for at most 3 chapters' detailed outlines**
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.