'data' 검색 결과

Prepare spreadsheet data for analysis by finding extra spaces, inconsistent capitalization, numbers stored as text, mixed date formats, and duplicates. Review proposed fixes, apply traceable formula-based changes, and compare results before and after cleanup.

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• Cleaning spreadsheet data before analysis
• Standardizing dates and text capitalization
• Finding duplicate rows and mixed data types

Guide the design and review of software that handles personal data using GDPR engineering practices. Covers minimal data collection, access controls, logging, encryption, retention, deletion, and checks for code changes and infrastructure.

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• Reviewing APIs and data models that handle personal data
• Checking logs for sensitive information
• Designing retention and user data deletion processes

Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.

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• Building scheduled data collection and transformation workflows
• Designing a data warehouse for analysis
• Synchronizing database changes in real time

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

Create realistic sample data for testing, development, and demos. Specify fields, record counts, value ranges, and business rules, then receive CSV, JSON, SQL INSERT statements, or a Python generator script with validation and usage instructions.

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• Creating test data that follows business rules
• Preparing realistic sample data for demos
• Populating a test database

Generate Python code to clean, transform, analyze, visualize, and model BigQuery data with BigQuery DataFrames. The skill keeps processing in the cloud and uses familiar table operations, with guidance for previews, schema checks, and machine learning.

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• Clean and transform BigQuery data with Python
• Preview data and check column types
• Visualize aggregated or sampled data

Helps developers send conversion and event data to Google Ads, Google Analytics, and Floodlight through the Data Manager API. It covers destination setup, data formatting, validation, migration from other APIs, and checking processing results.

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• Implement offline conversion uploads to Google Ads
• Send recommended or custom GA4 events
• Migrate event ingestion from another Google API

Find missing data in connected sources, inspect table structure, and suggest loading options for analysis. It also helps prepare a connection form for a new source, leaving connection and loading choices to the user.

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• Find external tables needed for an analysis
• Browse available connected data sources
• Check table columns before choosing data

Automate Crustdata operations through Rube MCP, a connection for agent tools. The skill searches for suitable actions, checks their current input requirements and the service connection, then executes the discovered tools.

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• Finding tools for a Crustdata task
• Checking required inputs before execution
• Running bulk operations

Automate Datarobot operations through Rube MCP, a connection for agent tools. The skill discovers available actions and their required inputs, checks the service connection, and runs the selected tools.

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• Find tools for a Datarobot task
• Run multiple supported operations in bulk
• Retrieve all pages of a tool response

Automate College Football Data operations through Rube MCP and Composio. Find tools for a requested task, check their input requirements and connection status, then execute operations individually or in batches.

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• Find tools for a College Football Data task
• Verify the connection before running an operation
• Process multiple operations in a batch

Automate Big Data Cloud operations through Rube MCP, a connection for agent tools. The skill finds available actions, checks their required inputs and connection status, and executes them using Composio's toolkit.

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• Finding available Big Data Cloud tools
• Checking current tool input requirements
• Automating operations after verifying the connection

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

Guide changes and reviews in datafusion-ffi, which connects separately compiled libraries. Check binary compatibility, memory ownership, and method coverage, and choose unit and cross-library tests for new wrappers, trait extensions, or codec changes.

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• Add FFI wrappers for DataFusion traits
• Check that default method overrides cross the FFI boundary
• Review binary compatibility and memory ownership in pull requests

Supports authorized security exercises that test data transfer outside a network. Organizes channel choices, compression, encryption, splitting, and receipt verification, alongside the monitoring signals defenders can use to identify different transfer methods.

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• Comparing data transfer channels for an authorized exercise
• Planning compression, encryption, and file splitting
• Verifying received files against SHA-256 records

Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift. Triggers on: find the table, where is our data, which table has, locate dataset, find data for, search catalog, what tables match, Redshift table, lakehouse table, data lake table, warehouse table, reverse lookup S3 path. Do NOT use for: full catalog audits (use exploring-data-catalog), running queries (use querying-data-lake), creating tables (use creating-data-lake-table).

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Process use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".

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Add another file (resource) to an EXISTING dataset in a PortalJS portal — a data dictionary, methodology, or an additional data file. Turns a single-file dataset into a multi-resource one; the showcase renders a section per resource. Use when a dataset needs a second file, such as a data dictionary, methodology doc, or an additional period's data.

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Search DigiKey for electronic components and download datasheets — primary source for prototype orders and the preferred API method for fetching datasheets. Find parts by keyword or MPN, check pricing/stock, download datasheets via API, analyze specifications. Sync and maintain a local datasheets directory — extract components from schematics, download missing datasheets, keep them up to date. Also supports batch MPN-list seeding (`--mpn-list`) for bulk workflows without a KiCad project. Use when the user asks about electronic components, part specs, datasheets, pricing, stock, footprints, or needs to download a datasheet — even without mentioning "DigiKey". Also for "sync datasheets", "download datasheets for my board/project", or mentions a datasheets directory. DigiKey is the default distributor for prototyping. For BOM workflows, see the bom skill.

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Search DigiKey for electronic components and download datasheets — primary source for prototype orders and the preferred API method for fetching datasheets. Find parts by keyword or MPN, check pricing/stock, download datasheets via API, analyze specifications. Sync and maintain a local datasheets directory — extract components from schematics, download missing datasheets, keep them up to date. Also supports batch MPN-list seeding (`--mpn-list`) for bulk workflows without a KiCad project. Use when the user asks about electronic components, part specs, datasheets, pricing, stock, footprints, or needs to download a datasheet — even without mentioning "DigiKey". Also for "sync datasheets", "download datasheets for my board/project", or mentions a datasheets directory. DigiKey is the default distributor for prototyping. For BOM workflows, see the bom skill.

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Guide for accessing financial and economic data in MATLAB using the Datafeed Toolbox. Covers Bloomberg (market data via bloomberg/blp/bloombergHypermedia), FRED (Federal Reserve economic data via fredrs), Haver Analytics (economic data via haver/haverdirect/haverview), and LSEG Datastream (historical data via datastreamws). Use when connecting to any of these data providers from MATLAB.

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Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

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Expert GDPR compliance assistant covering all four core workflows: (1) auditing code and systems for GDPR violations, (2) drafting GDPR-compliant documents such as privacy policies, Data Processing Agreements (DPAs), and consent notices, (3) answering GDPR compliance questions with authoritative article citations, and (4) reviewing data flows and PII handling practices. Use this skill whenever the user mentions GDPR, data protection, privacy compliance, lawful basis, data subject rights, DPA, privacy notices, consent management, data breaches, DPIAs, controller/ processor relationships, cross-border data transfers, or any EU/UK data privacy topic. Also trigger for questions like "is this GDPR compliant?", "how do I handle personal data?", "what does a privacy policy need?", or any request involving PII, personal data, or data retention in a regulatory context.

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Expert GDPR compliance assistant covering all four core workflows: (1) auditing code and systems for GDPR violations, (2) drafting GDPR-compliant documents such as privacy policies, Data Processing Agreements (DPAs), and consent notices, (3) answering GDPR compliance questions with authoritative article citations, and (4) reviewing data flows and PII handling practices. Use this skill whenever the user mentions GDPR, data protection, privacy compliance, lawful basis, data subject rights, DPA, privacy notices, consent management, data breaches, DPIAs, controller/ processor relationships, cross-border data transfers, or any EU/UK data privacy topic. Also trigger for questions like "is this GDPR compliant?", "how do I handle personal data?", "what does a privacy policy need?", or any request involving PII, personal data, or data retention in a regulatory context.

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