「query」の検索結果

[Deprecated] This is the required documentation for agents operating on the CloudBase Relational Database through MCP. It defines the canonical SQL management flow with `queryMysqlDatabase`, `manageMysqlDatabase`, `queryPermissions`, and `managePermissions`, including destroy flow, async status checks, safe query execution, schema initialization, and permission updates. MySQL provisioning is no longer available through MCP; new environments should use PostgreSQL — see postgresql-development skill instead.

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Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data freshness check", "validate data quality", "use DuckDB in Fabric".

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Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.

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When you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Tool-agnostic in design but defaults to a vault at ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}/. Six modes — capture (drop something into raw/), compile (process unprocessed raw files into wiki pages, update INDEX.md), query (answer a question from the wiki, save to outputs/), lint (orphans / contradictions / stale / unprocessed raw / topic gaps), connect (suggest new wikilinks between pages), search (quick lookup). Triggers on "/second-brain," "/sb," "capture this," "save this to my brain," "compile the wiki," "process raw notes," "query my wiki," "ask my brain," "lint the wiki," "find connections," "search my notes." Complements deep-research (external corpus) — this is the internal corpus.

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SQL and NoSQL query optimization techniques, indexing strategies, execution plan analysis, JOIN algorithms, cardinality estimation, and database-specific query patterns

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Diagnose database latency with explain plans, index ownership, and query-shape review. Use when a query is slow, an index is missing, or scans and N+1 patterns appear.

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SQL query review for correctness, performance, and best practices. Activate when a query needs review before production use, shows unexpected results, or runs too slowly.

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Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.

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Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".

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Fix ClickHouse query errors when querying AggregatingMergeTree tables that use state functions. Use when: (1) Query fails with type mismatch on AggregateFunction columns, (2) Using sum/count on columns created with sumState/countState/uniqState, (3) Creating views that JOIN with materialized views using AggregatingMergeTree, (4) Getting unexpected results from aggregate columns that show as AggregateFunction(sum, ...) type. The *State() functions store intermediate aggregate states, not final values - you must use *Merge() functions to finalize them.

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Fix ClickHouse query errors in Rust when using clickhouse-rs crate. Use when: (1) "string is not valid utf8" errors - typically FixedString columns need CAST to String, (2) "tag for enum is not valid" errors - typically Option<T> fields receiving non-NULL values, (3) Sum/count aggregations returning Float64 but Rust expects u64, (4) LEFT JOIN results with empty strings where Rust expects Option::None, (5) "not enough data, probably a row type mismatches a database schema" - query SELECT returns fewer columns than the Rust Row struct expects (common when multiple query functions share the same struct but one function is missing columns added later), (6) Garbled/corrupted data when INSERTing to FixedString(N) columns using String Rust type - silently corrupts data (no error!) because String adds a length prefix but FixedString expects exactly N raw bytes. Fix: use [u8; N] with #[serde(with = "BigArray")] from serde-big-array, (7) "the trait bound `[u8; 64]: serde::Serialize` is not satisfied" when using [u8; N] arrays in Row structs - need #[serde(with = "BigArray")] annotation from serde-big-array crate.

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TanStack Query v5 (React Query) server state management. Use for data fetching, caching, mutations, or encountering v4 migration, stale data, invalidation errors.

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Pinia Colada data fetching for Vue/Nuxt with useQuery, useMutation. Use for async state, query cache, SSR, or encountering invalidation, hydration, TanStack Vue Query migration errors.

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Quickly extract a typed GPU-Wiki query intent from prose. Never inspect or query a knowledge store.

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Query, filter, and transform Markdown structurally with mq — a jq-like CLI for Markdown. Use to extract headings/sections/code-blocks/links from .md files, build a table of contents, pull code blocks of a given language, slice or reshape LLM prompt/output Markdown, or batch-transform docs. Triggers on "extract sections from this markdown", "get all the code blocks", "jq for markdown", "mq", or any structural query over Markdown that grep/Read can't do cleanly.

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Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for Tier 1 research data. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".

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Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.md.

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Neo4j JavaScript/TypeScript Driver v6 — driver lifecycle, executeQuery, managed transactions (executeRead/executeWrite), session.run, Integer handling, JSON serialization, record access, async/await patterns, TypeScript types, error handling, and connection setup for Node.js and browser. Use when writing JS/TS code that connects to Neo4j — neo4j.driver(), executeQuery, executeRead, executeWrite, session.run, neo4j.int, record.get, Integer, BookmarkManager, bolt://, neo4j+s://. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver version migration — use neo4j-migration-skill.

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Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

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Query Etsy products with multi-dimensional filters (keyword/URL, price, sales, favorites, reviews, listing date, category, handmade/vintage types, Pick/Bestseller/Raving tags). Trigger when user mentions Etsy products, Etsy listings, Etsy product sourcing, Etsy bestsellers, Etsy handmade, Etsy vintage, Etsy items, or Etsy product query — even if the tool name is not mentioned, as long as the need is to search products, view sales/price/tags, or filter items on Etsy.

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Query Etsy stores with multi-dimensional filters (sales, favorites, reviews, store opening date, country, primary category, Raving/Star ratings). Trigger when user mentions Etsy store, Etsy shop search, Etsy seller, Etsy shop ranking, Etsy weekly sales stores, Etsy stores, or Etsy store query — even if the tool name is not mentioned, as long as the need is to find stores, filter store data, or analyze store performance on Etsy.

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Retrieve Amazon product details by ASIN, including price, title, main image, listing date, material, weight, variant monthly sales, and up to 12 months of monthly sales history. Triggered when users query Amazon product details, ASIN lookup, product pricing, sales rank history, monthly sales trends, product dimensions, FBA fees, product specifications, batch ASIN query, Nexscope product details, ASIN detail lookup, monthly sales data, pricing info, product specifications, FBA fees, batch ASIN query. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves obtaining structured product data for one or more Amazon ASINs.

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Query Amazon product detail and historical trends by ASIN using Nexscope data, covering 14 marketplaces. Trigger when the user mentions Nexscope product detail, ASIN detail query, sales trends, price curves, price history, BSR ranking history, BSR trends, profit analysis, FBA fee analysis, gross margin, product trend analysis, daily/monthly sales, revenue trends, Deal promotion history, product detail, sales trend, price history, BSR ranking, profit analysis, FBA fees. Even if the user does not explicitly mention "Nexscope", if their need involves querying Amazon product detail or historical trend data by ASIN, this skill should also be triggered.

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