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Found 2,461 Skills
AI-first knowledge base and startup OS with file-based storage, AI agents, scheduled jobs, and embedded apps
Standard Gear/Vara Sails builder pack for AI agents. Use when building or extending a Sails app on Vara or Gear. NOT for Vara.eth, ethexe, non-Sails programs, or generic protocol research.
Comprehensive security auditor for AI agent skills, prompts, and instructions. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you use any agent or skill.
Framework for automated search over task-specific model harnesses — the code around a fixed base model that decides what to store, retrieve, and show while the model works.
Command-line interface for Libreoffice - A stateful command-line interface for document editing, producing real ODF files (ZIP archives with ...
Smart Money Signal Copy Trade v1.0 — onchainos Agentic Wallet + Cost-Aware TP + Dev/Bundler Safety + Session Risk Control. Triggers when the user mentions smart money strategy, signal strategy, copy trading, whale tracking, KOL copy trading, on-chain signal trading, co-riding addresses, take profit / stop loss, risk preference, or wants to automatically buy/sell based on smart money signals. Runtime file: bot.py (includes Web Dashboard http://localhost:3248) Config file: config.py (hot-reload)
Explains the ADK Dev Console — what each tab shows, how to read Agent Steps, traces, and other UI features visible at localhost:3001 during adk dev
网文写作 skill 包，覆盖长篇与短篇网络小说的扫榜、拆文、写作、去AI味全流程
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.
Information Question Generator. Given an article, paper, or book, extract its core viewpoints into Q-A pairs — Questions get straight to the point, no textbook-style phrasing; Answers are concise and clear, with formalized conclusions and complete logical chains. As readers follow the Q chain, each Answer drives home a key point, reproducing the author's entire reasoning process. Activate when the user says '问答', 'Q&A', 'QA', '提问', '抽取问题', '/ljg-qa', or shares an article, paper, or book and requests Q-A extraction. This tool triggers when the user wants ideas extracted not as a summary but as a sequence of incisive questions paired with answers. NOT FOR FAQ generation, glossary creation, or comprehension quizzes — this is intellectual scaffolding, not a study aid.
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**
Platform-neutral guidance for using Open Browser Use, the open-source Chrome automation stack for AI agents. Use when an agent needs to install, verify, troubleshoot, or operate Open Browser Use through its browser extension, native CLI, JavaScript SDK, Python SDK, Go SDK, or Browser Use style JSON-RPC methods; use for tasks involving real Chrome tabs, user tab claiming, CDP commands, downloads, file choosers, clipboard helpers, or session cleanup.