'data write' 검색 결과

Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

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Draft Appwrite blog posts around concrete developer problems and search intent. Structure the article, prepare titles, summaries, FAQs, and article metadata, check product claims against local documentation, and generate a cover image using Appwrite's cover API.

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• Draft a practical Appwrite tutorial
• Outline a comparison article around search intent
• Prepare titles, summaries, FAQs, and article metadata

Professional content generation system with a GSD-style phased workflow, integrated SEO optimization (claude-seo), and anti-AI auditing (humanizer). USE THIS SKILL whenever the user asks to write, draft, create, generate, or produce ANY content — including blog posts, articles, LinkedIn posts, Facebook posts, Twitter/X tweets or threads, Instagram captions, email newsletters, email campaigns or sequences, landing pages, product pages, web pages, sales pages, sales funnels, case studies, testimonials, product descriptions, or SEO metadata. Also triggers on: "/writer:discuss", "/writer:plan", "/writer:execute", "/writer:verify", "/writer:ship", "/writer:next", "/writer:profile-create", "/writer:profile-view", "/writer:profile-edit", "/writer:status", "/writer:help", "/writer:update" — or any request to manage a writer profile or create marketing materials. ALWAYS use this skill even for quick, casual content requests like "write me a LinkedIn post about X" or "draft a tweet thread." The profile and phased workflow produce significantly better output than ad-hoc generation.

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Professional content generation with phased workflow (discuss→plan→execute→verify→ship), SEO optimization, and anti-AI auditing. Multi-profile support for unlimited brands. USE for: blog posts, LinkedIn, Twitter/X, Facebook, Instagram, email newsletters, landing pages, sales pages, case studies, product descriptions, SEO metadata. COMMANDS: /writer:discuss, /writer:plan, /writer:execute, /writer:verify, /writer:ship, /writer:next, /writer:profile-create, /writer:profile-list, /writer:profile-use, /writer:profile-view, /writer:profile-edit, /writer:status, /writer:help ALWAYS use for content requests like "write me a LinkedIn post" or "draft a blog article."

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Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.

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Manage cognee database migrations, from checking migration status and recovering blocked writes to creating schema or data updates. It also guides dataset backups, restores, and transfers using cognee’s portable COGX archive format.

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• Recover writes blocked by a failed migration
• Create migrations for database structure or stored data
• Back up and restore datasets with COGX

Define a dataset's metadata profile — infer a Frictionless Table Schema from its data, add Data Package metadata (license, sources, keywords), and write it into datasets.json so the showcase renders a typed field table. Extend or customize via the L0-L3 profile ladder. Use when a registered dataset needs field types, constraints, or catalog metadata before publishing.

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onboarding workflow for creating datasets and applications in Viking AI Search. Supports one-time import from local files (JSON, JSONL, CSV) and MySQL databases, plus scheduled incremental sync for append-only JSONL files and MySQL. All sources are first exported to a bootstrap JSONL file; backend-driven schema inference handles detection, and optional background sync keeps the dataset up to date as new data arrives.

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Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.

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• Read and write h5ad or Zarr datasets
• Combine experimental batches with source labels
• Select cells or genes using metadata

Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.

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• Build a pipeline to transform and aggregate large datasets
• Investigate slow Spark jobs and uneven data distribution
• Tune memory settings and data partitions

Use when reviewing a DataFusion Comet pull request that touches Iceberg writes, the split-operator write plan (IcebergWriteStrategy, IcebergWriteExec, IcebergCommitExec), the native Iceberg writer (CometIcebergWriteExec, iceberg_write.rs, iceberg_partition_path.rs), its eligibility gate (CometIcebergNativeWrite), or the iceberg-rust pin. Load alongside review-comet-pr.

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Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

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Turn application requirements into an Azure Cosmos DB NoSQL data model. Gather read and write patterns, workload volumes, and relationships, then document container structures, partition keys for distributing data, indexes, and design trade-offs in two Markdown files.

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• Designing a data model for a new application
• Choosing how to group and partition related data
• Reviewing database costs for high-volume workloads

Set up users, organizations, roles, and dataset sharing in cognee. The skill explains SDK and HTTP authentication, helps investigate permission errors or missing datasets, and clarifies how access settings separate each user’s stored data.

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• Share datasets with specific users or roles
• Organize data access across teams and organizations
• Configure SDK identities and HTTP authentication

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

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• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

End-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures. Make sure to use this skill whenever the user wants to run any empirical analysis, write analysis code, or produce output from data. Triggers include: "analyze this data", "run a regression", "write R code for this", "write Python code for this", "I have a dataset", "help me with this regression", "run a DiD", "run an RDD", "event study", "IV regression", "fit a model", "produce a table", "make a figure", "explore my data", or any request involving a dataset path or empirical estimation.

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Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data management plan", "DMP", "DMSP", "NIH data sharing plan", "write the data plan for my grant", or when a grant proposal needs a data-management section. NOT a submission tool — produces a draft the user pastes into the funder portal (DMPTool, NIH ASSIST, Horizon Europe portal).

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Review Appwrite blog posts for search visibility and clear answers that answer engines can extract. Measure titles and content, assess structure and links, and receive evidence-based findings with prioritized fixes and suggested replacements.

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• Checking a blog post before publication
• Reviewing titles, descriptions, and headings
• Assessing search intent and direct answers

Design agents that read, update, summarize, or sync spreadsheet data. Define how they select the right table, check existing values, verify writes, request confirmation for destructive changes, and report the exact rows and ranges processed.

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• Design an agent that refreshes a leads sheet each morning
• Build weekly sales reports from spreadsheet data
• Define an agent that syncs data between two systems

Turn plain language requirements into SQL, investigate slow queries, document database structures, and prepare migration templates with rollback plans. Support everyday database development and ORM use, where libraries connect application code to database tables.

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• Writing SQL for reports and data retrieval
• Investigating slow queries and repeated database calls
• Preparing schema changes and rollback plans

Answer business data questions with Wren CLI by checking model definitions, reusing past queries, and executing SQL or predefined aggregations. The workflow also covers query verification, error diagnosis, and saving successful queries for future use.

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• Analyze monthly revenue trends
• Rank customers by purchase value
• Compare metrics across plans or regions

Prepare Bifrost releases by turning Git history into release summaries and module changelogs. Coordinate version updates across dependent modules, document database migration risks, and include closed issues and outside contributor credits.

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• Preparing changelogs for a Bifrost release
• Coordinating version updates across dependent modules
• Documenting database migration rollback limits

Migrate (harvest) datasets between open-data platforms. Reads CKAN, a DCAT-US /data.json catalog (DKAN, ArcGIS Hub, data.gov), a DCAT / DCAT-AP RDF feed (JSON-LD, Turtle, or RDF/XML), Socrata, OpenDataSoft, or an ArcGIS FeatureServer, and writes them to a static PortalJS catalog or pushes them into a CKAN instance over its API. Use when moving datasets from an external open-data platform into a PortalJS portal, or bridging one CKAN instance to another.

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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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