「tables」の検索結果

Create a Snowflake materialization to stream Estuary collections into Snowflake tables. Use when setting up Snowflake as a destination for captured data. Use when user says "send to Snowflake", "materialize to Snowflake", "Snowflake destination", or "load into Snowflake".

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Create an Estuary derivation that flattens a nested array into one output document per array element. Use for normalising nested JSON, exploding line items, unpacking tags, or producing one-row-per-item tables from arrays. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (some destinations can `UNNEST` server-side without one). Use when user says "flatten-array derivation", "derivation to explode an array", "transformation to unnest", "derive one row per item", or "flatten nested data with a derivation".

日本語の概要は準備中です。原文の説明を表示しています。

Create a Databricks materialization to stream Estuary collections into Databricks tables via Unity Catalog. Use when setting up Databricks as a destination for captured data. Use when user says "send to Databricks", "materialize to Databricks", "Databricks destination", or "load into Databricks".

日本語の概要は準備中です。原文の説明を表示しています。

Create a BigQuery materialization to stream Estuary collections into BigQuery tables. Use when setting up BigQuery as a destination for captured data. Use when user says "send to BigQuery", "load into BigQuery", "materialize to BigQuery", or "BigQuery destination".

日本語の概要は準備中です。原文の説明を表示しています。

Use when a project follows a structured operating model with: two work lanes (Domain, System), unresolved work tracked as Epic, bounded execution tracked as Ticket, role-based behavior (architect, runner), helper-backed SQLite workflow tables, inbox and outbox files as task and result bodies.

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Automate Coda tasks via Rube MCP (Composio): manage docs, pages, tables, rows, formulas, permissions, and publishing. Always search tools first for current schemas.

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Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing...

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Extract text, tables, and structured data from documents using Azure Document Intelligence (@azure-rest/ai-document-intelligence). Use when processing invoices, receipts, IDs, forms, or building cu...

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Build document analysis applications with Azure Document Intelligence (Form Recognizer) SDK for Java. Use when extracting text, tables, key-value pairs from documents, receipts, invoices, or buildi...

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Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.

日本語の概要は準備中です。原文の説明を表示しています。

Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.

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Per-surface tech stack decision tables and development tooling options. Use during /create-prd-stack to present constraint-filtered options to the user.

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Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for any database paradigm.

日本語の概要は準備中です。原文の説明を表示しています。

TanStack Virtual headless virtualization for React. Use when rendering large lists (100+ items), implementing virtual scroll, building infinite scroll feeds, virtualizing grids or tables, using window-level scrolling, or implementing masonry/lane layouts with @tanstack/react-virtual. Triggers on: useVirtualizer, useWindowVirtualizer, virtual list, virtual scroll, list virtualization.

日本語の概要は準備中です。原文の説明を表示しています。

Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Aws Dynamodb Deep — aWS DynamoDB deep-dive management covering table configurations, capacity analysis, GSI/LSI health, auto-scaling policies, streams, backups, contributor insights, and TTL settings. Use when performing deep analysis of DynamoDB tables, optimizing capacity and throughput, debugging throttling, or auditing table configurations beyond basic inventory.

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Use when working with Apache Pinot — apache Pinot management — monitor cluster health, tables, segments, ingestion jobs, query performance, and controller/broker/server status. Use when debugging ingestion issues, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

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Word (.docx) manipulation via MCP server. Use for reading, creating, editing, formatting Word documents including tables, footnotes, comments, images, headers, styles, and PDF conversion.

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Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.

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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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Excel (.xlsx) manipulation via MCP server. Use for creating workbooks, formatting cells, writing formulas, building charts, pivot tables, data analysis, or any task involving Excel spreadsheets.

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Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.

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Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.

日本語の概要は準備中です。原文の説明を表示しています。