“data”的搜索结果

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

Retrieve price and trading volume data for US, Hong Kong, and Canadian stocks, ETFs, and indices through Yahoo Finance. Also gather company profiles, financial statements, dividends, and ownership data for market research and historical strategy testing.

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• Collect historical prices across several stock markets
• Review company financial statements and ownership
• Check dividend and stock split histories

Plan and implement database structure changes and data transfers using Sequelize, TypeORM, or Prisma. Covers staged changes to keep services running, rollback procedures to undo changes, backups, and migration verification.

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• Add, rename, or change database columns
• Transform data into a new structure
• Prepare and test rollback procedures

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

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

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

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• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Match Google Cloud data residency and access requirements to available data boundary frameworks and cloud controls. Compare ranked recommendations in a table, see requirement gaps, and receive deployment guidance with built-in frameworks prioritized.

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• Finding frameworks for data residency requirements
• Comparing framework coverage and major requirement gaps
• Planning deployment for existing or new projects

Assess how changes, failures, or paused updates to a BigQuery table or view affect downstream assets. Trace data lineage, the relationships showing where data flows, and summarize dependent tables, dashboards, and processes with direct and indirect impacts.

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• Assess downstream effects of a table failure
• Check dependencies before changing or deleting a view
• Evaluate the impact of pausing data updates

Manage BigQuery datasets, tables, views, and jobs, and run SQL queries for basic data analysis. Provides setup examples and references for data ingestion, command-line operations, client libraries, access permissions, and infrastructure configuration.

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• Create BigQuery datasets and tables
• Search and aggregate data with SQL
• Load data and manage processing jobs

Design GraphQL APIs with clear data types, data-fetching functions called resolvers, and real-time updates. Address repeated database calls, pagination, structured errors, and expensive queries while planning schema changes that preserve compatibility.

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• Designing schemas around business concepts
• Reducing repeated database calls with DataLoader
• Planning real-time subscriptions over WebSocket

Create a Wren MDL project, a model layer for database queries, from an existing database. Discover tables and relationships, normalize column types, generate YAML files, and validate and build the project before indexing its schema.

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• Create a Wren project from an existing database
• Add selected schemas and tables as a new data source
• Define model relationships from database constraints

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.

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• Add SQL benchmarks for DataFusion operators
• Select representative input sizes and filter conditions
• Reduce unrelated processing overhead in measurements

Build TypeScript summary panels that combine data already fetched by other panels. Accept partial updates, wait for required fields before rendering, and update displayed metrics without additional network requests, using reusable base classes and a coordinator.

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• Combine email and stock metrics in one summary panel
• Wait for required data before showing a summary
• Update metrics using data fetched by other panels

Implement data loading and API submissions in Blazor, including loading indicators, error handling, and cancellation when navigation changes. Configure HttpClient and organize data services for components that run on the server or in the browser.

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• Load API data into a Blazor page
• Show saving status and prevent duplicate submissions
• Cancel and reload data when route parameters change

Retrieve economic data from official statistics and established research sources after confirming price basis, seasonal adjustment, frequency, and currency. Return structured tables with source details and access dates, plus loading guidance for the user’s statistical software.

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• Retrieving GDP or employment data from official sources
• Confirming consistent specifications for country comparisons
• Finding replication data for published research

Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.

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• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures

Maintain Tika's registry of metadata keys, the names used for document attributes. Guides regeneration after key changes, checks generated files against code, applies naming rules, and finds outdated references after renaming.

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• Adding a Tika metadata key
• Renaming keys and finding outdated references
• Investigating metadata schema test failures

Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).

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Full inventory and audit of AWS Glue Data Catalog assets across S3 Tables, Redshift-federated, and remote Iceberg catalogs. Triggers on: inventory the catalog, audit databases, list all tables, catalog overview, data landscape, enumerate catalogs, data inventory, search the catalog. Do NOT use for finding specific data (use finding-data-lake-assets), running queries (use querying-data-lake), or creating tables (use creating-data-lake-table).

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Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.

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Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.

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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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Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).

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