1305 个 · 按相关度排序
Style UI for postman.com using extracted design tokens, named values for colors, corner radius, and fonts. Reuse the supplied settings and identify missing values when the available tokens do not cover a styling need.
中文简介正在准备中。当前显示英语简介。
Scaffold and operate Claude Blog Brain, a source-cited Obsidian brain for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill. Use when the user says "claude-blog-brain", "Claude Blog Brain", "create a blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill brain", "import sources", "synthesize plan", "render report", or wants a persistent vault-backed operating system for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill.
中文简介正在准备中。当前显示原文说明。
Map a MetricFlow semantic_model or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table, `extends:` inheritance flattening, metric-type handling (simple / derived / ratio / cumulative / conversion), `model: ref('x')` resolution, and four worked examples. Load when the turn contains `.yml`/`.yaml` files with top-level `semantic_models:` or `metrics:`.
中文简介正在准备中。当前显示原文说明。
Manages Power BI semantic models, including tables, columns, measures, relationships, field parameters, DAX, Import, DirectQuery and Direct Lake designs, sources, connections, permissions, refresh, and deployment. Load this skill before acting on a semantic model, unless it is part of publishing a PBIP project, which powerbi-report-cli owns. For natural-language data questions use fabriciq; for visuals use powerbi-report-cli.
中文简介正在准备中。当前显示原文说明。
Organize a Power BI or Analysis Services semantic model end to end so it is ready for report authors, Excel users and Copilot - table groups, display folders, Title Case business names and naming conventions, hierarchies, sort-by columns, hiding, Analyze in Excel (IsAvailableInMDX), descriptions, format strings, data categories, context annotations, DAX formatting and UDFs, calculated-object justification, duplicate removal, perspectives, translations and security roles. Use whenever the user asks to "organize", "tidy", "clean up", "polish", "prepare", "standardize" or "make report-ready" a model or one table in it (for example "organize the date table"), to "fix naming conventions", "rename fields", "make names human readable" or "fix abbreviations", even when they name only one of these aspects, and whenever a model is driven through the Tabular Editor 3 MCP or the te CLI for that purpose. Not for DAX performance tuning (dax skill) or model design and star-schema changes (semantic-model skill).
中文简介正在准备中。当前显示原文说明。
Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.
中文简介正在准备中。当前显示原文说明。
This skill should be used when refactoring existing CSS from inline styles or utility classes to semantic patterns. Triggers on "refactor CSS", "extract styles", "consolidate CSS", "convert inline", "clean up styles", "migrate to semantic". Transforms to semantic classes with dark mode and tests.
中文简介正在准备中。当前显示原文说明。
Identify bug-introducing commits using semantic analysis that extends traditional SZZ algorithm. Distinguishes semantic changes from refactorings or code movements using control-flow and data-flow similarity analysis. Use when analyzing bug-fix commits to trace back to bug-introducing changes, investigating software evolution, conducting empirical studies on defect prediction, or reducing false positives in bug localization. Supports git repositories and provides explanations for why commits are identified as bug-introducing.
中文简介正在准备中。当前显示原文说明。
Analyzes and verifies semantic equivalence between two functions, classes, or modules by examining control flow, data flow, and observable behavior. Use when comparing code implementations (refactored vs original, different implementations of same functionality, migration verification), determining if two code artifacts produce identical behavior, identifying behavioral differences between code versions, or validating that code changes preserve semantics. Supports formal reasoning and symbolic execution approaches.
中文简介正在准备中。当前显示原文说明。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.
中文简介正在准备中。当前显示原文说明。
Assists with storing, searching, and managing vector embeddings using ChromaDB. Use when building RAG pipelines, semantic search engines, or recommendation systems. Trigger words: chromadb, chroma, vector database, embeddings, semantic search, similarity search, vector store, rag.
中文简介正在准备中。当前显示原文说明。
High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.
中文简介正在准备中。当前显示原文说明。
Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.
中文简介正在准备中。当前显示原文说明。
Build evidence-labeled semantic cores for SEO, LLM-readable architecture, and site information design. Use this skill when the user asks for semantic core, query clusters, search intents, audience segments, entity/topic maps, language or locale priorities, competitor/source evidence, pillar-topic planning, or a structured handoff before URL architecture and internal linking. It must mark unknown search volume, difficulty, and ranking data as unknown instead of inventing metrics.
中文简介正在准备中。当前显示原文说明。
Build a measured semantic core for a brand through official keyword APIs (Yandex Wordstat for RU, Google Ads Keyword Planner / Bing Webmaster worldwide, autocomplete everywhere), commit it as core.json plus a questions.csv, and hand that straight to an open-geo visibility run. Use when the user wants demand research, a semantic core, keyword volume, or "collect the questions and then measure visibility" — no browser and no manual keyword tool.
中文简介正在准备中。当前显示原文说明。
"Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.".
中文简介正在准备中。当前显示原文说明。
Scaffold and operate Claude Blog Brain, a source-cited Obsidian brain for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill. Use when the user says "claude-blog-brain", "Claude Blog Brain", "create a blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill brain", "import sources", "synthesize plan", "render report", or wants a persistent vault-backed operating system for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill.
中文简介正在准备中。当前显示原文说明。
Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python. Use when working on applications, plugins, or function-calling flows built on Semantic Kernel.
中文简介正在准备中。当前显示原文说明。
Examine every existing page on the site, build a semantic map across pillars, and add missing internal links so the site becomes a semantic graph anchored on clear topical pillars. Use this skill whenever the user asks to "add internal links", "build internal linking", "build a semantic graph", "link related pages", "strengthen topical authority", "fix orphan pages", "add pillar linking", or wants hub-and-spoke linking between pillars, hubs, and supporting content. Output is an edit list (proposed link insertions) plus optionally direct edits to page source files.
中文简介正在准备中。当前显示原文说明。
legacy workaround を避け、semantic HTML / Web Platform Native API / modern CSS の利用機会を提示する。
中文简介正在准备中。当前显示原文说明。
使用Pinecone构建按内容含义查找信息的搜索基础设施，支持数据写入、更新与删除、条件筛选，以及语义搜索与关键词搜索的组合。
使用AgentDB实现按含义查找文档的搜索功能，将文档存储、相似度搜索与属性筛选结合起来，构建检索相关资料并据此回答问题的系统。
按语义相近程度或关键词搜索Ruflo中保存的任务背景、解决方案和用户纠正意见。可跨存储分区搜索、选择排序方式，并确认实际采用的搜索方式及回退情况。
将工作中有效的解决方案和成功做法保存到AgentDB，再按语义相似度检索复用，支持开工前查找既有经验，以及完成后记录成功做法和新收获。