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Found 12,037 Skills
MindOS is the user's local knowledge assistant and shared knowledge base. It keeps decisions, meeting notes, SOPs, debugging lessons, architecture choices, research findings, and preferences available across sessions and agents. 更新笔记, 搜索知识库, 整理文件, 执行SOP/工作流, 复盘, 追加CSV, 跨Agent交接, 路由非结构化输入到对应文件, 提炼经验, 同步关联文档. NOT for editing app source, project docs, or paths outside the KB. Core concepts: Space, Instruction (INSTRUCTION.md), Skill (SKILL.md); notes can embody both. Trigger on: save or record anything, search for prior notes or context, update or edit a file, organize notes, run a workflow or SOP, capture decisions, append rows to a table or CSV, hand off context to another agent, check if something was discussed before, look up a past decision, distill lessons learned, prepare context for a meeting, quick-capture to staging area, organize inbox, check knowledge health, detect conflicts or contradictions, find stale content. Chinese triggers: 帮我记下来, 搜一下笔记, 更新知识库, 整理文件, 复盘, 提炼经验, 保存, 记录, 交接, 查一下之前的, 有没有相关笔记, 把这个存起来, 放到暂存台, 整理暂存台, 知识健康检查, 检测知识冲突. Proactive behavior — do not wait for the user to mention MindOS: (1) When user's question implies stored context may exist (past decisions, previous discussions, meeting records) → search MindOS first, even if they don't explicitly mention it. (2) After completing valuable work (bug fixed, decision made, lesson learned, architecture chosen, meeting summarized) → offer to save it to MindOS for future reference. (3) After a long or multi-topic conversation → suggest persisting key decisions and context.
This skill should be used when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation, attention patterns, context clash, context confusion, or agent performance degradation. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of diagnosing and mitigating context failures.
Monitor STX stacking positions — status, PoX cycles, reward payouts, and delegation eligibility for autonomous agents.
Paired benchmark orchestration for comparing coding-agent performance with recursive-mode off and on. Use when the user wants to benchmark recursive-mode, compare recursive vs non-recursive execution on the same project, generate disposable benchmark repos, capture timing/build-test logs, or write a benchmark report.
Design patterns for the Langroid multi-agent LLM framework. Covers agent configuration, tools, task control, and integrations.
Create, improve, and manage Droid skills. Use when the user wants to: - Create new skills from scratch or from session learnings - Improve existing skills based on user preferences - Analyze sessions to identify patterns worth codifying - Understand best practices for agentic skill design This is a meta-skill for self-improvement and continuous learning.
Charlie Munger's Mental Lattice applied to a business idea. Spawns a team of specialist agents — Mathematician, Psychologist, Inverter, Economist, Moat Analyst — who each apply their discipline's elementary models to the idea. The lead synthesizes into a lollapalooza analysis: which forces stack, which fight you, and the honest Munger verdict. Use when the user says "munger this", "apply the lattice", "what would Charlie think", or proposes a business idea and wants multidisciplinary analysis. Works as a standalone analysis or after /office-hours.
USE FOR web search, research, RAG, grounding, browse, find, lookups, fact-checking, documentation, agentic AI. All-in-one, optimized for AI agents. Pre-extracted, token-budgeted web content, deep research, news, images, videos, places, custom ranking
Review requirements or plan documents using parallel persona agents that surface role-specific issues. Use when a requirements document or plan document exists and the user wants to improve it.
Comprehensive pull request review using specialized agents
Wire a semantic layer into a nao agent so that metric queries are routed through a single source of truth. Supports dbt MetricFlow (dbt Cloud with Semantic Layer), Snowflake (views or semantic views via MCP), an in-house nao YAML semantic layer, or other tools (via MCP discovery). Installs the right MCP server, updates RULES.md to route metric queries through the semantic layer, and (for the nao YAML option) generates starter metric files. Use after a first round of tests has shown the agent struggling with metric reliability. Do not use for raw rule writing (write-context-rules) or first-time setup (setup-context).
Novel chapter content creation, suitable for user requests such as "Write a chapter of a novel for me", "Continue the following content", "Generate XX plot", "Batch write web novel chapters", "Expand/rewrite this content", "Write me an XX plot", "Continue the novel", "Expand this content", "Rewrite this chapter", "Batch generate novel chapters", "Write an opening chapter", "Write a climax plot", "Novel content generation", "Help me write novel content", etc. It supports multiple modes such as single-chapter/multi-chapter batch generation, continuation, rewriting, and expansion. It automatically adapts to the rhythm of web novels, maintains consistency of characters and plot, and **automatically uses sub-Agents for parallel processing during batch generation, with each Agent responsible for a maximum of 3 chapters**