「query」の検索結果

Both user [{"tool": "web_search", "params": {"query": "

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{"tool": "web_search", "params": {"query": "Continuous Learning Loop: Fehler-Capture +

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We need to parse user query. They ask: "Find the structural connection between these two situations:

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1. **Deconstruct the User's Query**: * Core subject 1: Energy efficiency (in computing/AI).

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Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).

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Extract clean Markdown or text from one or more known URLs through Tavily. Use when the user supplies specific pages and needs their content, including query-focused chunks or JavaScript-rendered pages.

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Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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Convert approved Stitch exports into accessible React and Vite dashboards with DESIGN.md tokens, TanStack Query data boundaries, responsive layouts, and optional read-only Web3 integrations.

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Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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React 19+ with TypeScript — hooks, custom hooks, state management (useState/useReducer/useContext), React Query/SWR, Tailwind CSS, performance. Use when building React components, apps, or optimizing renders.

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NotebookLM MCP server management - query notebooks, add from share links, handle auth, reset sessions. Use when working with Google NotebookLM notebooks for conversational research tasks.

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Zero-config Postgres for Netlify apps via @netlify/database — querying data from Functions/Edge Functions, writing schema migrations, setting up Drizzle ORM, local dev with netlify dev, database branches for deploy previews, and migrating an existing Postgres project onto Netlify. Use when adding a database, building a contact form or CRUD API, writing SQL migrations, wiring up Drizzle, running netlify database commands, testing with a local Postgres, or switching from Neon/Supabase/RDS to Netlify Database.

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Cache dynamic and static responses on Netlify's CDN from Functions, Edge Functions, and proxies. Use when you add caching or cache-control headers to a function response, tune cache TTL or stale-while-revalidate, set up the durable cache, vary a cache key by query/header/cookie/country/language, purge or invalidate the cache by site or cache tag, use the programmatic Cache API (caches.open/match/put) or @netlify/cache helpers (fetchWithCache/cacheHeaders/getCacheStatus), speed up an expensive API call, add ISR or on-demand revalidation, or debug why a response is or isn't cached via the Cache-Status header.

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Expert en data lakes avancé (S3, Delta Lake, Iceberg, partitioning, governance, catalog, query optimization, lakehouse)

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Canonical patterns for writing custom MSBuild targets. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing custom target authoring anti-patterns, reviewing MSBuild target definitions for correctness, diagnosing broken SDK target chains across files (e.g., Directory.Build.targets silently redefining SDK targets), fixing targets that replace CompileDependsOn instead of extending it with $(CompileDependsOn), fixing query targets that return stale results due to Outputs vs Returns misuse, fixing missing Inputs/Outputs causing unnecessary rebuilds, fixing missing FileWrites registration. Covers DependsOnTargets vs BeforeTargets vs AfterTargets, the Build→CoreBuild three-level pattern, hooking into the build pipeline, the $(XxxDependsOn) chain-extension pattern. DO NOT USE FOR: incremental build tuning (use incremental-build), parallelization (use build-parallelism), general anti-patterns (use msbuild-antipatterns), non-MSBuild build systems.

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Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.

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Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.

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SQL query optimization, schema design, indexing strategies, and relational database mastery for production data systems

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Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

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Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture

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Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures.

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Guidelines for developing with Kysely, a type-safe TypeScript SQL query builder with autocompletion support

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PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.

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Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

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