[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-mongodb-mongodb-search-and-ai-zh-CN":3,"guides-for-mongodb-mongodb-search-and-ai":515,"similar-k1724c581pj4qca8wdevmzkr0586n034-zh-CN":516},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":240,"isFallback":225,"parentExtension":244,"providers":301,"relations":305,"repo":307,"tags":512,"workflow":513},1778694413837.2078,"k1724c581pj4qca8wdevmzkr0586n034",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"指导 MongoDB 用户实现和优化 Atlas Search（全文搜索）、Vector Search（语义搜索）和 Hybrid Search 解决方案。当用户需要为文本查询（自动完成、模糊匹配、分面搜索）、语义相似性（嵌入、RAG 应用）或组合方法构建搜索功能时，请使用此技能。当用户需要文本包含、子字符串匹配（“包含”、“包括”、“出现在”）、不区分大小写或多字段文本搜索，或跨多个字段进行具有可变组合的过滤时，也请使用此技能。提供有关选择正确的搜索类型、创建索引、构建查询和使用 MongoDB MCP 服务器优化性能的工作流。\n",{"claudeCode":12},"mongodb/agent-skills","mongodb-search-and-ai","https://github.com/mongodb/agent-skills",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":223,"workflow":238},1778694413837.208,"kn71st9fygc2zxs0qss7bmpb2x86n171","zh-CN",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":204,"promptVersionScoring":205,"purpose":206,"rationale":207,"score":208,"summary":209,"tags":210,"tier":217,"useCases":218},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","描述清楚地指出了指导 MongoDB 用户实现和优化 Atlas Search 解决方案的问题，涵盖了文本、向量和混合搜索。",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","该技能提供了结构化的工作流和有关选择搜索类型、创建索引和优化查询的指导，这超出了基本的 API 交互，并提供了比简单提示更大的价值。",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","该技能涵盖了从发现和索引创建到查询和优化的完整生命周期，并提供清晰的工作流和版本兼容性检查，表明其已准备好投入生产使用。",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","该技能专注于 MongoDB Atlas Search 的实现和优化，涵盖词汇、向量和混合搜索，范围明确且一致。",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","显示的描述准确地反映了该技能的功能，结构良好，并清楚地概述了它指导用户完成的搜索功能类型。",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","该技能利用了范围限定的工具，例如 `list-databases`、`collection-schema`、`collection-indexes`、`atlas-inspect-cluster`、`create-index` 和 `aggregate`，而不是单个通才命令。",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","索引创建、查询和版本检查的所有相关参数均在 SKILL.md 和参考文件中进行了记录。",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","使用的工具（例如 `create-index`、`aggregate`）具有描述性，并遵循动词-名词约定。",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","该技能的输入和输出由其调用的 MCP 工具和请求/返回的结构化数据明确定义，避免了不必要的数据膨胀。",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","从捆绑的 LICENSE 文件检测到，该扩展是在 Apache-2.0（一种宽松的开源许可证）下许可的。",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","上次提交是在 2026-05-11，在过去 90 天内。",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","未检测到需要特殊管理的第三方依赖项。",{"category":66,"check":67,"severity":63,"summary":68},"Security","Secret Management","该技能不处理机密信息，因为它依赖 MCP 服务器进行身份验证，而 MCP 服务器是外部配置的。",{"category":66,"check":70,"severity":24,"summary":71},"Injection","该技能不加载外部数据或从远程 URL 获取数据，并且它将用户输入视为数据而不是指令。",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","该技能仅在其捆绑的内容上运行，并且在运行时不获取外部代码或数据。",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","该技能通过定义的 MCP 工具与环境进行交互，并且不尝试修改其指定范围之外的文件。",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","在技能的逻辑中未发现拒绝的工具调用周围的分离进程生成或重试循环。",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","该技能不包含读取或将机密数据提交给第三方的指令。",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","捆绑的内容不包含隐藏的转向技巧，并且所有描述都使用干净的可打印 ASCII。",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","该技能的逻辑包含在可读的 SKILL.md 文本中，不涉及混淆或最小化代码。",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","该技能的工作流假定标准的 MongoDB Atlas 交互，不依赖于特定的用户项目文件布局。",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","在过去 90 天内，开放和关闭的 issue 数量均为 0，表明近期活动或 issue 数量较少。",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","该存储库包含 CHANGELOG.md 并在其安装说明（例如 `mongodb-mcp-server@1`）中使用语义化版本控制，表明发布管理良好。",{"category":104,"check":105,"severity":24,"summary":106},"Code Execution","Validation","该技能依赖 MCP 服务器的工具模式进行输入验证，参考文件提供了结构化的查询模式。",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","在创建索引之前，该技能需要用户明确批准，并且在没有用户确认的情况下不执行任何破坏性操作。",{"category":104,"check":111,"severity":24,"summary":112},"Error Handling","该技能的工作流包括版本兼容性检查，并提示用户批准，表明在意外状态下会采取关闭失败（fail-closed）的行为。",{"category":104,"check":114,"severity":63,"summary":115},"Logging","该技能本身不执行需要本地审计日志记录的破坏性操作或出站调用；这些由 MCP 服务器处理。",{"category":117,"check":118,"severity":63,"summary":119},"Compliance","GDPR","该技能操作 MongoDB 架构和数据结构，而不是直接操作个人数据，并且不将数据提交给第三方。",{"category":117,"check":121,"severity":24,"summary":122},"Target market","该技能广泛适用于全球的 MongoDB Atlas 用户，未检测到区域限制。",{"category":92,"check":124,"severity":24,"summary":125},"Runtime stability","该技能依赖标准的 MongoDB MCP 服务器工具和常用 shell 命令，确保了跨平台兼容性。",{"category":44,"check":127,"severity":24,"summary":128},"README","README 提供了清晰的安装说明，适用于各种环境，并链接到相关文档。",{"category":33,"check":130,"severity":24,"summary":131},"Tool surface size","该技能使用了一组与定义范围相关的小型、专注的 MCP 工具。",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","该技能为特定操作使用了不同的 MCP 工具，避免了冗余或重叠的功能。",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","所有宣传的功能，如实现和优化 Atlas Search、Vector Search 和 Hybrid Search，都由提供的工作流和参考文件支持。",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","README 提供了适用于多个平台的全面安装说明，并包括 MCP 服务器的设置指南。",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","该技能的工作流和参考文件描述了版本不匹配和缺失索引等错误情况，并提供了补救措施。",{"category":147,"check":148,"severity":63,"summary":149},"Execution","Pinned dependencies","该技能没有包含需要固定或解释器声明的外部依赖项的捆绑脚本。",{"category":33,"check":151,"severity":24,"summary":152},"Dry-run preview","该技能在执行索引创建命令之前需要用户明确批准，起到了某种形式的试运行预览作用。",{"category":154,"check":155,"severity":63,"summary":156},"Protocol","Idempotent retry & timeouts","该技能依赖 MCP 服务器和 MongoDB 操作进行重试和超时；它不实现自己的重试和超时。",{"category":117,"check":158,"severity":63,"summary":159},"Telemetry opt-in","该技能不发送遥测数据；如果实现了遥测功能，则由底层代理或 MCP 服务器处理。",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","目的定义精确，指出它指导 MongoDB 用户实现和优化 Atlas Search、Vector Search 和 Hybrid Search 解决方案。",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","SKILL.md 的 frontmatter 简洁，有效地总结了该技能的核心能力和用例。",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","SKILL.md 的正文结构清晰，分为不同部分，并将详细程序委托给参考文件，长度合理。",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","特定搜索类型（词汇、向量、混合）的详细程序已适当地拆分到单独的参考文件中，并从主 SKILL.md 链接，实现了渐进式披露。",{"category":170,"check":174,"severity":63,"summary":175},"Forked exploration","该技能的工作流是指导性的，不涉及广泛的探索或深度代码审查，因此不需要 `context: fork`。",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","参考文件提供了清晰的端到端示例，涵盖了各种搜索模式，包括索引定义和聚合管道。",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","该技能记录了故障模式和限制，例如混合搜索的版本检查以及处理缺失字段或索引，并提供了恢复步骤。",{"category":104,"check":183,"severity":63,"summary":184},"Tool Fallback","该技能设计用于与 MongoDB MCP 服务器配合使用，MCP 服务器是必需组件，没有备用路径。",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","该技能明确指示在不满足混合搜索的版本要求时停止并通知用户，并在继续创建索引前需要用户批准。",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","该技能是独立的，不依赖于其他特定技能的加载；它清楚地定义了与 MongoDB Atlas Search 相关的范围。",1778694322161,"该技能指导用户在 MongoDB 中实现、优化和排查 Atlas Search（词汇）、Vector Search（语义）和 Hybrid Search。它涵盖了选择正确的搜索方法、创建索引、构建查询以及优化性能，并为每种搜索类型提供了详细的参考文件。",[195,196,197,198,199],"指导实现 Atlas Search、Vector Search 和 Hybrid Search","根据用例推荐合适的搜索类型","提供索引创建和查询构建的工作流","包含词汇、向量和混合搜索的详细参考文件","提供有关版本检查和查询优化的指导",[201,202,203],"执行通用的数据库管理任务","取代 MongoDB Atlas UI 进行索引管理（提供 JSON 以供手动创建）","执行搜索和索引之外的任意数据库查询","3.0.0","4.4.0","通过提供清晰的指导和工作流，使 MongoDB 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Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.","atlas-stream-processing",{"claudeCode":12},"SKILL.md frontmatter at skills/atlas-stream-processing/SKILL.md",[368,370,372,374,376,378,380],{"path":369,"priority":321},"SKILL.md",{"path":371,"priority":344},"references/connection-configs.md",{"path":373,"priority":344},"references/development-workflow.md",{"path":375,"priority":344},"references/mcp-troubleshooting.md",{"path":377,"priority":344},"references/output-diagnostics.md",{"path":379,"priority":344},"references/pipeline-patterns.md",{"path":381,"priority":344},"references/sizing-and-parallelism.md",{"basePath":383,"description":384,"displayName":385,"installMethods":386,"rationale":387,"selectedPaths":388,"source":322,"sourceLanguage":253,"type":243},"skills/mongodb-connection","Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.","mongodb-connection",{"claudeCode":12},"SKILL.md frontmatter at skills/mongodb-connection/SKILL.md",[389,390],{"path":369,"priority":321},{"path":391,"priority":344},"references/monitoring-guide.md",{"basePath":393,"description":394,"displayName":395,"installMethods":396,"rationale":397,"selectedPaths":398,"source":322,"sourceLanguage":253,"type":243},"skills/mongodb-mcp-setup","Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.","mongodb-mcp-setup",{"claudeCode":12},"SKILL.md frontmatter at skills/mongodb-mcp-setup/SKILL.md",[399],{"path":369,"priority":321},{"basePath":401,"description":402,"displayName":403,"installMethods":404,"rationale":405,"selectedPaths":406,"source":322,"sourceLanguage":253,"type":243},"skills/mongodb-natural-language-querying","Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks \"how do I query...\", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.","mongodb-natural-language-querying",{"claudeCode":12},"SKILL.md frontmatter at skills/mongodb-natural-language-querying/SKILL.md",[407],{"path":369,"priority":321},{"basePath":409,"description":410,"displayName":411,"installMethods":412,"rationale":413,"selectedPaths":414,"source":322,"sourceLanguage":253,"type":243},"skills/mongodb-query-optimizer","Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: \"How do I optimize this query?\", \"How do I index this?\", \"Why is this query slow?\", \"Can you fix my slow queries?\", \"What are the slow queries on my cluster?\", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.","mongodb-query-optimizer",{"claudeCode":12},"SKILL.md frontmatter at skills/mongodb-query-optimizer/SKILL.md",[415,416,418,420,422],{"path":369,"priority":321},{"path":417,"priority":344},"references/aggregation-optimization.md",{"path":419,"priority":344},"references/antipattern-examples.md",{"path":421,"priority":344},"references/core-indexing-principles.md",{"path":423,"priority":344},"references/update-query-examples.md",{"basePath":425,"description":426,"displayName":427,"installMethods":428,"rationale":429,"selectedPaths":430,"source":322,"sourceLanguage":253,"type":243},"skills/mongodb-schema-design","MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on \"design schema\", \"embed vs reference\", \"MongoDB data model\", \"schema review\", \"unbounded arrays\", \"one-to-many\", \"tree structure\", \"16MB limit\", \"schema validation\", \"JSON Schema\", \"time series\", \"schema migration\", \"polymorphic\", \"TTL\", \"data lifecycle\", \"archive\", \"index explosion\", \"unnecessary indexes\", \"approximation pattern\", \"document versioning\".","mongodb-schema-design",{"claudeCode":12},"SKILL.md frontmatter at skills/mongodb-schema-design/SKILL.md",[431,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466],{"path":369,"priority":321},{"path":433,"priority":344},"references/antipattern-excessive-lookups.md",{"path":435,"priority":344},"references/antipattern-unnecessary-collections.md",{"path":437,"priority":344},"references/antipattern-unnecessary-indexes.md",{"path":439,"priority":344},"references/fundamental-document-model.md",{"path":441,"priority":344},"references/fundamental-document-size.md",{"path":443,"priority":344},"references/fundamental-embed-vs-reference.md",{"path":445,"priority":344},"references/fundamental-schema-validation.md",{"path":447,"priority":344},"references/pattern-approximation.md",{"path":449,"priority":344},"references/pattern-archive.md",{"path":451,"priority":344},"references/pattern-attribute.md",{"path":453,"priority":344},"references/pattern-bucket.md",{"path":455,"priority":344},"references/pattern-computed.md",{"path":457,"priority":344},"references/pattern-document-versioning.md",{"path":459,"priority":344},"references/pattern-extended-reference.md",{"path":461,"priority":344},"references/pattern-outlier.md",{"path":463,"priority":344},"references/pattern-polymorphic.md",{"path":465,"priority":344},"references/pattern-schema-versioning.md",{"path":467,"priority":344},"references/pattern-time-series-collections.md",{"basePath":241,"description":469,"displayName":13,"installMethods":470,"rationale":471,"selectedPaths":472,"source":322,"sourceLanguage":253,"type":243},"Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.\n",{"claudeCode":12},"SKILL.md frontmatter at skills/mongodb-search-and-ai/SKILL.md",[473,474,476,478,480],{"path":369,"priority":321},{"path":475,"priority":344},"references/hybrid-search.md",{"path":477,"priority":344},"references/lexical-search-indexing.md",{"path":479,"priority":344},"references/lexical-search-querying.md",{"path":481,"priority":344},"references/vector-search.md",{"basePath":483,"description":484,"displayName":485,"installMethods":486,"rationale":487,"selectedPaths":488,"source":322,"sourceLanguage":253,"type":243},"tools/review-skill","Review a proposed Agent Skill for structural validity and content quality before publishing. Runs the skill-validator CLI to check for structural issues, scores the skill with an LLM judge, and interprets results to advise SMEs on what to address. Use when a user wants to review, validate, or quality-check an Agent Skill.","review-skill",{"claudeCode":12},"SKILL.md frontmatter at tools/review-skill/SKILL.md",[489,490,493,495],{"path":369,"priority":321},{"path":491,"priority":492},"assets/report.md","low",{"path":494,"priority":344},"references/install-skill-validator.md",{"path":496,"priority":344},"references/llm-scoring.md",{"sources":498},[499],"manual",{"closedIssues90d":8,"description":260,"forks":231,"license":235,"openIssues90d":8,"pushedAt":232,"readmeSize":229,"stars":233,"topics":501},[502,503,504,505,506,507],"agent","claude","cursor","gemini-cli-extension","mcp","skills",{"classifiedAt":509,"discoverAt":510,"extractAt":511,"githubAt":511,"updatedAt":509},1778694148853,1778694144419,1778694146756,[212,215,214,211,216,213],{"evaluatedAt":514,"extractAt":284,"updatedAt":239},1778694322274,[],[517,540,570,593,614,645],{"_creationTime":518,"_id":519,"community":520,"display":521,"identity":525,"providers":526,"relations":534,"tags":536,"workflow":537},1778694375752.5935,"k176q0fcrs34d0qgcw5zcfr7e186mppv",{"reviewCount":8},{"description":522,"installMethods":523,"name":524,"sourceUrl":14},"为任何支持的驱动程序语言优化 MongoDB 客户端连接配置（池、超时、模式）。在处理/更新/审查实例化或配置 MongoDB 客户端（例如，调用 `connect()` 时）、配置连接池、对连接错误（ECONNREFUSED、超时、池耗尽）进行故障排除、优化与连接相关的性能问题时，请使用此技能。这包括构建具有 MongoDB 的无服务器函数、创建使用 MongoDB 的 API 端点、优化高流量 MongoDB 应用程序、创建长期运行任务和并发性，或调试与连接相关的失败等场景。",{"claudeCode":12},"MongoDB Connection Optimizer",{"basePath":383,"githubOwner":211,"githubRepo":242,"locale":18,"slug":385,"type":243},{"evaluate":527,"extract":533},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":528,"targetMarket":271,"tier":217},[211,215,529,530,531,532],"connection","performance","optimization","configuration",{"commitSha":274,"license":235},{"parentExtensionId":246,"repoId":280,"translatedFrom":535},"k175wmq2n17n23xzphj2zzt3qs86n3xd",[532,529,215,211,531,530],{"evaluatedAt":538,"extractAt":284,"updatedAt":539},1778694243014,1778694375752,{"_creationTime":541,"_id":542,"community":543,"display":544,"identity":550,"providers":554,"relations":563,"tags":566,"workflow":567},1778696691708.3108,"k174bsewq8k21t0bg3rrzcxkcd86mrgx",{"reviewCount":8},{"description":545,"installMethods":546,"name":548,"sourceUrl":549},"Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search",{"claudeCode":547},"ruvnet/ruflo","agentdb-query","https://github.com/ruvnet/ruflo",{"basePath":551,"githubOwner":552,"githubRepo":553,"locale":253,"slug":548,"type":243},"plugins/ruflo-agentdb/skills/agentdb-query","ruvnet","ruflo",{"evaluate":555,"extract":562},{"promptVersionExtension":204,"promptVersionScoring":205,"score":556,"tags":557,"targetMarket":271,"tier":217},99,[558,215,559,216,560,506,561],"agentdb","query","knowledge-graph","llm",{"commitSha":274},{"parentExtensionId":564,"repoId":565},"k1702kbgkcgg2way9x5303rpr186n62a","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[558,215,560,561,506,559,216],{"evaluatedAt":568,"extractAt":569,"updatedAt":568},1778699766010,1778696691708,{"_creationTime":571,"_id":572,"community":573,"display":574,"identity":578,"providers":581,"relations":589,"tags":590,"workflow":591},1778696691708.307,"k176zwpf986zp7jmtfwp20fnfh86mcws",{"reviewCount":8},{"description":575,"installMethods":576,"name":577,"sourceUrl":549},"Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).",{"claudeCode":547},"V3 Memory Unification",{"basePath":579,"githubOwner":552,"githubRepo":553,"locale":253,"slug":580,"type":243},".claude/skills/v3-memory-unification","v3-memory-unification",{"evaluate":582,"extract":587},{"promptVersionExtension":204,"promptVersionScoring":205,"score":556,"tags":583,"targetMarket":271,"tier":217},[584,215,558,585,586,213],"memory","hnsw","migration",{"commitSha":274,"license":588},"MIT",{"repoId":565},[558,215,585,584,586,213],{"evaluatedAt":592,"extractAt":569,"updatedAt":592},1778699464598,{"_creationTime":594,"_id":595,"community":596,"display":597,"identity":601,"providers":603,"relations":610,"tags":611,"workflow":612},1778696691708.2996,"k175z5j3knm69exj7qpfwy3e1586nh3w",{"reviewCount":8},{"description":598,"installMethods":599,"name":600,"sourceUrl":549},"Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.\n",{"claudeCode":547},"embeddings",{"basePath":602,"githubOwner":552,"githubRepo":553,"locale":253,"slug":600,"type":243},".agents/skills/embeddings",{"evaluate":604,"extract":609},{"promptVersionExtension":204,"promptVersionScoring":205,"score":556,"tags":605,"targetMarket":271,"tier":217},[600,213,585,606,607,216,608],"sql-js","ai","retrieval",{"commitSha":274},{"repoId":565},[607,600,585,608,216,606,213],{"evaluatedAt":613,"extractAt":569,"updatedAt":613},1778698905205,{"_creationTime":615,"_id":616,"community":617,"display":618,"identity":624,"providers":628,"relations":638,"tags":641,"workflow":642},1778693180473.1177,"k179t7487ft90cyp6e4xypxsgh86nq5q",{"reviewCount":8},{"description":619,"installMethods":620,"name":622,"sourceUrl":623},"Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.\nTriggers: \"azure-search-documents\", \"SearchClient\", \"SearchIndexClient\", \"vector search\", \"hybrid search\", \"semantic search\".\n",{"claudeCode":621},"microsoft/agent-skills","Azure AI Search SDK for Python","https://github.com/microsoft/agent-skills",{"basePath":625,"githubOwner":626,"githubRepo":242,"locale":253,"slug":627,"type":243},".github/plugins/azure-sdk-python/skills/azure-search-documents-py","microsoft","azure-search-documents-py",{"evaluate":629,"extract":637},{"promptVersionExtension":204,"promptVersionScoring":205,"score":630,"tags":631,"targetMarket":271,"tier":217},95,[632,633,634,635,213,214,636],"azure","ai-search","sdk","python","semantic-search",{"commitSha":274,"license":588},{"parentExtensionId":639,"repoId":640},"k171mfx6atvhq1bkhpky84v4b186n9qd","kd77czgnv00rfjm815pcc5xx5986n5t8",[633,632,214,635,634,636,213],{"evaluatedAt":643,"extractAt":644,"updatedAt":643},1778695120589,1778693180473,{"_creationTime":646,"_id":647,"community":648,"display":649,"identity":653,"providers":655,"relations":663,"tags":665,"workflow":666},1778696691708.3264,"k179thjzaw5kepc7zhdj9sat3n86mcqp",{"reviewCount":8},{"description":650,"installMethods":651,"name":652,"sourceUrl":549},"Validate pending migrations for foreign key consistency, rollback safety, and best practices",{"claudeCode":547},"migrate-validate",{"basePath":654,"githubOwner":552,"githubRepo":553,"locale":253,"slug":652,"type":243},"plugins/ruflo-migrations/skills/migrate-validate",{"evaluate":656,"extract":662},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":657,"targetMarket":271,"tier":217},[215,658,659,660,661,270],"migrations","sql","validation","code-quality",{"commitSha":274},{"parentExtensionId":664,"repoId":565},"k176me0sh9b6bc3gzttnywx4w986njzh",[661,215,270,658,659,660],{"evaluatedAt":667,"extractAt":569,"updatedAt":667},1778701008912]