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Found 205 Skills
Analyze a codebase and generate comprehensive documentation including architecture, components, interfaces, workflows, and dependencies. Creates an AI-optimized knowledge base (index.md) and can consolidate into AGENTS.md, README.md, or CONTRIBUTING.md. Use when the user wants to document a codebase, create AGENTS.md, understand system architecture, generate developer documentation, or asks to "summarize the codebase".
Feishu Message Sending and Document Creation Workflow. Trigger Scenarios: When users mention "send Feishu message", "Feishu document", "notify someone", "send to Feishu", "Feishu notification". Applicable to: Sending Feishu messages, creating Feishu documents, operating Feishu Base, managing Knowledge Base.
Apply plugin knowledge base updates to an existing generated system. Consults the Ars Contexta research graph for methodology improvements, proposes skill upgrades with research justification. Never auto-implements. Triggers on "/upgrade", "upgrade skills", "check for improvements", "update methodology".
Interact with the Denser Retriever API to build and query knowledge bases. Use this skill whenever the user wants to create a knowledge base, upload documents (files or URLs), search/query a knowledge base, list or delete knowledge bases or documents, check document processing status, or check account usage/balance. Also trigger when the user mentions 'denser retriever', 'knowledge base', 'document search', 'semantic search', 'RAG pipeline', or wants to index and search their files.
企业微信客服自动化系统。自动同意好友添加、基于知识库的智能问答、未知问题人工介入提醒。适用于企业微信客服场景的 AI 助手机器人。
Add memories, learnings and context to OpenViking, aka. ov. Use when saving insights in chat. Trigger this tool when 1. sees keyword "ovm"; 2. is explicitly requested memorizing e.g. "remember ..." 3. identifies valuable memory worth adding
Search a knowledge base of recent research, news, and analysis spanning AI development, technology, business strategy, economics, and industry trends. Sources include tech blogs, X posts, podcast transcripts, earnings calls, and expert commentary. Use this skill whenever the user asks about recent developments, news, trends, what's happening in a field or with a company, technical topics in AI/ML, or wants a research briefing. Also use when the user mentions specific companies, technologies, industries, or economic topics and seems to want current information rather than general knowledge.
Brain health checks: back-link enforcement, citation audit, filing validation, stale info detection, orphan pages, and benchmarks. Use when asked to check brain health, run maintenance, or audit quality.
Document finalized technology selections, architecture decisions, long-term constraints, and coding conventions in the project into searchable permanent documents. No one will remember why X was chosen six months later, but with decision documents, at least the background can be understood before making changes next time. Four types: tech-stack (which tools/libraries/frameworks to use), architecture (how the system is organized), constraint (what is not allowed), convention (what is uniformly done). Trigger scenarios: Proactively push when important choices are made after feature-design or issue-analyze, or when the user says "record decision", "archive technology selection", "ADR", "record this constraint", "write down the convention". Only archive finalized decisions; do not archive under-discussion solutions.
Fill web forms by fetching form fields from a URL, deep-searching the user's local knowledge base for relevant info, and generating a markdown document with all answers pre-filled. Use when the user provides a URL to a web form (conference application, speaker submission, event registration, profile form) and wants help filling it out from their existing materials. Also trigger when the user mentions "填网页表", "fill web form", "网页填表", "表单填写", "申请表填写", "conference application", "speaker submission", "讲师申请", "报名表", or provides a URL with "form", "feedback", "apply", "register", "submit" in the path.
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or audit such a base, or initialize a new one. Trigger on phrases like "add this to my wiki", "ingest this paper", "compile this into the knowledge base", "what does my wiki say about X", "lint the wiki", "build a knowledge base from these documents", "research notes", "second brain", "personal knowledge base", or any reference to LLM Wiki / OmegaWiki. Trigger even when the user does not say "wiki" — if they are accumulating sources over time and want them organized, this applies. The skill scales — sharded indexes, atomic pages, YAML frontmatter, and a bundled search script keep the wiki from becoming a context bottleneck at hundreds or thousands of pages.
Organize research, discussions, and exploratory content into systematic knowledge documents, or collect and organize research information about companies/products. Use this skill when users request knowledge summarization, note organization, knowledge base document generation, or structuring discussion content into formal documents. It also applies to collecting and organizing research information about AI companies, startups, and products. Even if users don't explicitly mention "knowledge graph" or "knowledge base", this skill should be used for any workflow that involves sorting scattered information into systematic documents.