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Found 40 Skills
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
Index skill for the blockint-skills bundle—includes a “choosing a skill” routing map and routes to focused skills on blockchain intelligence fundamentals, address clustering, analytics, tokenomics, investigation ethics, Phalcon Compliance documentation pointer, Chainalysis public Sanctions API/oracle router, FATF official AML/CFT glossary, Arkham Intel research article on leading crypto analysis tools for traders, Christoph Michel cmichel.io guide on becoming an EVM smart contract auditor, risk exposure, behavioral risk, address and transaction screening workflow concepts, Range AI investigation playbook (MCP), crypto market mechanics, OSINT (Bellingcat toolkit), Solana external stacks (Helius, Range MCP, Tavily, PayAI, React Flow, Solana Policy Institute), DeFi/MEV/rug skills, privileged-access mitigation lessons (Chainalysis Drift case study), coral-xyz sealevel-attacks Solana security examples, Neodyme Solana Security Workshop (workshop.neodyme.io), Osec (osec.io) Solana auditor introduction blog post, canonical X post citation for @armaniferrante status 1411589629384355840, BlockchainSpider open-source data collection, MoTS (Know Your Transactions / transaction semantics research repo), Impersonator dApp devtools (EVM + Solana read-only address presentation), Katana web crawling, lcamtuf American Fuzzy Lop (AFL) classic documentation (lcamtuf.coredump.cx/afl), and the official Agent Skills open-format specification (agentskills/agentskills, agentskills.io/llms.txt doc index). Use when the task spans multiple topics or the user needs help picking which named skill to load.
Convert a course outline into a fully fleshed out intelligent textbook. Use when: (1) User provides a URL containing a course outline/syllabus (2) User asks to "generate a course" or "create a textbook" from an outline (3) User wants to expand a course description into full learning materials Generates: learning graph, chapter structure, MicroSim specifications, quizzes, glossary, and FAQ following the intelligent textbooks framework and evidence-based learning design principles.
Use this skill when the user wants to add or update metadata in DataHub: descriptions, tags, glossary terms, ownership, deprecation, domains, data products, structured properties, documents, or field-level metadata. Triggers on: "add tag to X", "update description for X", "set owner of X", "add glossary term", "deprecate X", "create a domain", "create a glossary term", "add a document", or any request to modify DataHub metadata.
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.
Help set up and troubleshoot rendering for task files and glossary files. Use when the user asks about PlantUML, AsciiDoc, VS Code, JetBrains, preview rendering, Java, or Graphviz needed to review rendered task or glossary files.
This skill automatically generates a comprehensive glossary of terms from a learning graph's concept list, ensuring each definition is precise, concise, distinct, non-circular, and free of business rules. Use this skill when creating a glossary for an intelligent textbook after the learning graph concept list has been finalized.
Set up a full AI ensemble/mob programming team for any software project. Creates team member profiles (.team/), coordinator instructions (.team/coordinator-instructions.md), project owner constraints (PROJECT.md), team conventions (AGENTS.md), architectural decisions (docs/ARCHITECTURE.md), domain glossary, and supporting docs. Use when: (1) starting a new project and wanting a full expert agent team, (2) the user asks to "set up a team", "create a mob team", "set up ensemble programming", or "create agent profiles", (3) converting an existing project to the driver-reviewer mob model, (4) the user wants AI agents to work as a coordinated product team with retrospectives and consensus-based decisions.
Creates Source-of-Truth docs (Project Brief, Decisions, Glossary) for new app ideas. Use at the very start of a project to lock scope, stack, and terminology. Essential for preventing drift in downstream PRD, UI/UX, and Architecture phases.
Explain core Contentful concepts and route users to the right implementation skill or documentation. Use when users ask conceptual questions, need terminology clarified, want help choosing between APIs (CDA/CMA/CPA/GraphQL), or need guidance on the Contentful MCP server. Also triggers on "Contentful 101", "which Contentful API", "how do I get started", "which skill should I use", "what does X mean in Contentful", "Contentful glossary", "CDA vs CPA", "CDA vs GraphQL", "how does Contentful work", "Contentful architecture", "explain environments", "what are aliases", "content model design", "headless CMS", "Contentful MCP", "MCP server", "set up MCP", "Remix Contentful", "Astro Contentful", "Gatsby Contentful", "SvelteKit Contentful", "Nuxt Contentful". Not for framework-specific implementation (contentful-nextjs), migrations (contentful-migration), personalization (contentful-personalization), or hands-on REST/GraphQL request examples (contentful-api).
This skill generates comprehensive metrics reports for intelligent textbooks built with MkDocs Material, analyzing chapters, concepts, glossary terms, FAQs, quiz questions, diagrams, equations, MicroSims, word counts, and links. Use this skill when working with an intelligent textbook project that needs quantitative analysis of its content, typically after significant content development or for project status reporting. The skill creates two markdown files - book-metrics.md with overall statistics and chapter-metrics.md with per-chapter breakdowns - in the docs/learning-graph/ directory.
When the user wants to create, optimize, or audit glossary page content and structure. Also use when the user mentions "glossary," "definitions," "terminology," "industry terms," "glossary page," "term definitions," "vocabulary," "glossary SEO," or "definition page."