'data' 검색 결과

Import SaaS data into DuckDB using dlt, a data loading tool, then generate a Wren project with table definitions and relationships. Verify the setup with real SQL queries, or start from an existing DuckDB file.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing HubSpot or Stripe data for SQL analysis
• Creating a Wren project from an existing dlt DuckDB file
• Generating table definitions and parent-child relationships

Automate Brightdata operations through Rube MCP and Composio. The skill discovers tools for a requested task, verifies the connection, and follows current input requirements to execute supported operations, including grouped workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding tools for a Brightdata task
• Checking the Brightdata connection before execution
• Running multiple supported operations together

Retrieve focused genome, assembly, and taxonomy metadata from NCBI Datasets v2, a biological data service. Get concise Markdown summaries by default, with raw JSON or text available when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking genome metadata for a specific organism
• Looking up an assembly by its accession identifier
• Checking taxonomy information by taxon ID

Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides compliance with India's Digital Personal Data Protection Act 2023. Covers consent manager registration, data fiduciary obligations under Sections 4-7, significant data fiduciary requirements under Section 10, data principal rights, and Board enforcement framework. Keywords: DPDP Act, India data protection, consent manager, data fiduciary, significant data fiduciary, data principal rights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenLineage, DataHub, Marquez for data lineage tracking and impact analysis. Activate on: data lineage, OpenLineage, DataHub, Marquez, impact analysis, data catalog, column lineage, data discovery. NOT for: data quality validation (use data-quality-guardian), dbt documentation (use dbt-analytics-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenLineage, DataHub, Marquez for data lineage tracking and impact analysis. Activate on: data lineage, OpenLineage, DataHub, Marquez, impact analysis, data catalog, column lineage, data discovery. NOT for: data quality validation (use data-quality-guardian), dbt documentation (use dbt-analytics-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Organize MRI, EEG, and other biomedical research data using BIDS, a shared standard for filenames, folders, and metadata. Query datasets, check compliance, convert DICOM files, and prepare metadata or processed outputs for analysis and sharing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize MRI and EEG files using BIDS conventions
• Find files by participant, session, or task
• Convert DICOM data and prepare metadata

Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert raw examples into training-ready JSONL
• Check conversation templates and assistant-only training targets
• Review example boundaries after sequence packing

Examine NTFS disk images for residual or hidden data beyond standard file recovery. Analyze file metadata, change records, unused space within allocated storage, and Alternate Data Streams to reconstruct available activity and identify suspicious timestamp differences.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find residual data in file slack space
• Investigate metadata left by deleted files
• Reconstruct recorded file changes

Provision a PostgreSQL database with Neon Instagres and save connection settings for application use and migrations. Includes setup examples for Drizzle and Prisma, initial data loading, and claiming a temporary database to keep using it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Provision a PostgreSQL database for development
• Configure database access with Drizzle or Prisma
• Load a SQL file with initial data

Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating errors in production
• Comparing error counts across time periods
• Following request traces across services

Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Standardizing instrument data for a laboratory management system
• Converting ELISA results into an importable CSV
• Exporting Python code to parse NanoDrop files

Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a data platform across multiple clouds
• Planning data ingestion and AI serving components
• Preparing infrastructure code and deployment instructions

Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up Wren and connect a database
• Load HubSpot or Stripe data through dlt
• Generate model definitions and add business metrics

Review Apache DataFusion pull requests using the project's review guide. Gather the proposed changes, discussion, and automated check results, or apply the same review criteria to your own changes before submitting a pull request.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an Apache DataFusion pull request
• Checking your changes before submitting a pull request
• Reviewing changes alongside discussion and automated check results

Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test a Beam pipeline locally
• Configure pipeline execution on Dataflow
• Review Flink or Spark execution settings

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert India Digital Personal Data Protection Act, 2023 (DPDPA) compliance advisor. Use this skill whenever a user asks about the DPDPA, DPDP Act, DPDP Rules 2025, India data privacy law, Data Fiduciary obligations, Data Principal rights, Significant Data Fiduciary, Data Protection Board of India, consent under DPDPA, notice requirements, breach notification India, children's data India, cross-border data transfer India, India privacy compliance, DPDPA gap analysis, DPDPA vs GDPR, or any obligation under India's personal data protection framework. Also trigger for: "Section 6 consent", "Section 7 legitimate uses", "Section 9 children's data", "Section 10 SDF", "Section 16 cross-border", "Rule 6 breach notification", "Rule 13 SDF obligations", "Data Protection Board complaint", "verifiable parental consent India", "DPDPA compliance roadmap", or "India privacy law global company".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Practitioner skill for advising on EU Regulation 2023/2854 (Data Act). Covers Chapters II-VII (IoT data access, mandatory B2B sharing, unfair contract terms, public-sector exceptional need, cloud switching, third-country governmental access) and Chapter VIII (interoperability and smart contracts, gate-only). Use when the user asks about Data Act rights or obligations, drafts a Data Act notice or letter, reviews a data-sharing or cloud-switching contract under the Data Act, runs a Data Act gap analysis, or asks how the Data Act interacts with GDPR, the DMA, the Trade Secrets Directive, or sectoral law. Triggers include "Data Act", "Datengesetz", "Regulation (EU) 2023/2854", "Art. 4(1) request", "Art. 5(1) third-party request", "trade-secret handbrake", "cloud switching obligations", "Chapter VI", "Ch V exceptional need", and references to specific Data Act articles or recitals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.