'data write' 검색 결과

Port MATLAB Data Acquisition Toolbox code from the discouraged (legacy) session-based interface (daq.createSession, addAnalogInputChannel, startBackground, DataAvailable listeners, queueOutputData, wait) to the recommended DataAcquisition interface (daq("ni"), addinput, start, ScansAvailableFcn, write, preload). Use when migrating legacy DAQ scripts, converting session-API calls, working with DataAcquisition objects, writing multi-feature DAQ scripts that combine triggers, callbacks, continuous acquisition, or analog output. Also use when a user says their DAQ script "used to work" or "errors on R20XX", since modernizing legacy session-API code is a frequent fix for those failures. Trigger keywords: daq, DAQ, NI, session interface, addtrigger, addclock, ScansAvailableFcn, ScansRequiredFcn, daq.createSession, addAnalogInputChannel, startBackground, queueOutputData, DataAvailable, evt.Data, evt.TimeStamps, wait(d), preload, discouraged, legacy, modernize, R2020a.

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Discover, explore, and query Databricks data via Genie — the CLI equivalent of the Genie One MCP. MUST be invoked whenever the user asks to find or locate data ('what tables are in X', 'where does X live', 'which catalog/schema has Y'), answer a natural-language question about the data, or write a SQL query.

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Create and update dashboards with charts, metrics, and tables drawn from connected data sources or attached files. Link displayed values to their sources so readers can inspect queries, calculations, and freshness while reviewing business indicators or recurring reports.

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• Track KPIs and progress toward targets
• Visualize CSV, TSV, or JSON data
• Build recurring reports from connected data

Inspect FCS files from flow cytometry, a method for measuring cell properties, using FlowIO. Extract measurement data into NumPy arrays, review channel metadata, export tables, and create FCS 3.1 files for downstream work.

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• Inspect measurement settings and channel labels in FCS files
• Extract measurement data into NumPy arrays or CSV tables
• Read legacy FCS files containing multiple datasets

Improve PostgreSQL performance through a staged workflow covering slow query analysis, indexes that speed up data access, query rewrites, configuration tuning, routine maintenance, and monitoring for production databases.

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• Investigating slow PostgreSQL queries
• Reviewing database indexes
• Tuning database configuration

Write complex SQL, improve slow queries, and design database schemas for transaction processing and analytics. Uses schema details and query execution plans to guide indexing, validate results, and assess performance with realistic data volumes.

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• Investigate and improve slow analytical queries
• Design a database schema for a SaaS application
• Write customer retention and cohort analysis queries

Turn a plain-language data request into SQL tailored to your database. Build queries that combine tables, filter records, and calculate summaries, with explanations of each step, performance considerations, and suggestions for adjusting dates or other conditions.

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• Create SQL for order counts by status
• Analyze retention by signup month
• Query large tables with date-based partitions

Inspect database connections and table structures configured in DBX, run read-only queries with row and time limits, and generate schema documentation or DBML, a text format for describing database structures.

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• Explore tables and columns in a DBX database
• Check data with bounded read-only queries
• Generate database structure documentation

Manage which n8n instance an MCP session targets when working across production, staging, teams, or clients. Guides instance switching, checks before credential changes, and recovery from unexpected missing data or ambiguous credential writes.

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• Switching between production and staging
• Checking the target before credential changes
• Investigating unexpected missing data

Write and troubleshoot n8n expressions, the formulas that pass and transform data between workflow steps. Covers field references, webhook inputs, dates, strings, and choosing between expressions and Code nodes for data transformations.

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• Fixing expression syntax errors
• Using webhook data in downstream steps
• Formatting dates and strings

Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.

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• Investigate a slow SQL query
• Write complex joins and grouped calculations
• Design tables, keys, and constraints

Create a structured Markdown report when multiple retrieval tools fail. Using available domain knowledge, the workflow separates verified facts from general guidance, marks missing data, and saves a document with limitations and practical next steps.

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• Drafting a report when search and file reading fail
• Organizing general guidance into a structured document
• Marking missing data for later completion

Search, create, and update records in Notion databases after checking the exact database and its fields. Preview proposed changes, preserve unrelated values, and verify the saved record with its URL or identifier.

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• Find records using the database’s actual fields
• Preview changes before creating or updating records
• Verify saved records and retrieve their links

Generate dark-mode-compatible inline SVG data visualization charts for blog posts. Supports horizontal bar, grouped bar, donut, line, lollipop, area, and radar charts with automatic platform detection (HTML vs JSX/MDX). Enforces chart type diversity, accessible markup (role=img, aria-labelledby), source attribution, and transparent backgrounds. Use whenever the user mentions data visualization, charts, graphs, comparison tables that need to be visualized, or wants to embed inline SVG visualizations in a blog post, even if not invoking blog-write. Use when user says "blog chart", "generate chart", "data visualization", "svg chart", "blog graph", "visualize data", or when the blog-write workflow identifies chart-worthy data points (3+ comparable metrics, trends, before/after data).

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Generate dark-mode-compatible inline SVG data visualization charts for blog posts. Supports horizontal bar, grouped bar, donut, line, lollipop, area, and radar charts with automatic platform detection (HTML vs JSX/MDX). Enforces chart type diversity, accessible markup (role=img, aria-label), source attribution, and transparent backgrounds. Use whenever the user mentions data visualization, charts, graphs, comparison tables that need to be visualized, or wants to embed inline SVG visualizations in a blog post, even if not invoking blog-write. Use when user says "blog chart", "generate chart", "data visualization", "svg chart", "blog graph", "visualize data", or when the blog-write workflow identifies chart-worthy data points (3+ comparable metrics, trends, before/after data).

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Generate publication-ready methods sections, figure legends, supplementary tables, and data availability statements from ENCODE analysis provenance. Implements the scientific documentation standards requiring complete metadata reporting. Use when the user needs to write methods, generate figure legends, create supplementary tables, draft data availability statements, compile tool citations, or auto-generate any publication text from their ENCODE analysis. Trigger on: methods section, figure legend, supplementary table, data availability, tool citations, publication writing, manuscript, write methods, methods draft, write up, write-up, paper writing, reproducible methods.

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Windows アプリ開発 (Windows App SDK / WinRT) のファイル・設定・データ保存リファレンス。 Windows.Storage の StorageFile, StorageFolder, IStorageItem, FileIO, PathIO, CachedFileManager, DataReader, DataWriter, BasicProperties。ApplicationData, ApplicationDataContainer, ApplicationDataCompositeValue のローカル・ローミング設定。 FileOpenPicker, FileSavePicker, FolderPicker, KnownFolders, StorageApplicationPermissions (FutureAccessList / MostRecentlyUsedList)。 JsonObject, JsonArray, XmlDocument, SQLite, EF Core, Microsoft.Data.SqlClient (SQL Server), Cosmos DB, MySQL, MongoDB 接続。

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Inspect microscopy data stored on OMERO.server, export selected image metadata, and plan imports or exports. The skill uses explicit data scopes and limits, with dry-run helpers and reviewed targets for workflows that change server data.

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• Inventory selected OMERO objects with result limits
• Export annotations and regions of interest as JSON
• Plan microscopy image imports and exports

Guide Prisma 8 development by selecting task-specific references for data definitions, queries, migrations that change database structure, runtime setup, debugging, and upgrades. Check package versions and generated contract files before proceeding with implementation.

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• Set up Prisma 8 or connect it to an existing database
• Edit data definitions and plan database migrations
• Write queries and configure database connections

Migrate the database access layer of a .NET/C# application from Oracle to PostgreSQL with Npgsql. Update packages, connection settings, parameter types, SQL, and stored procedure calls, then build the copied project and track completed migration tasks.

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• Migrate C# database access from Oracle to PostgreSQL
• Replace Oracle packages and connection settings
• Review parameter types and stored procedure calls

Write and improve C# unit tests with MSTest 3.x/4.x. Covers assertions that check expected results, tests with multiple input sets, asynchronous setup, cleanup, and test organization, with guidance on APIs that differ between versions.

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• Create a C# unit test project
• Test multiple input sets with DataRow or DynamicData
• Update exception checks to modern assertion APIs

Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.

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• Designing tables for large analytical datasets
• Improving slow aggregation queries
• Analyzing user retention and conversion steps

Plan and manage Cloud Spanner databases on Google Cloud, design tables and primary keys, write SQL and client code, and investigate slow queries using database statistics to identify resource-intensive operations.

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• Create Cloud Spanner instances and databases
• Design primary keys and related tables
• Write SQL queries and client library code