648건 · 관련도순
Design n8n automations for service integrations, scheduled tasks, databases, and AI agents. Choose from six workflow patterns, plan data flow and error handling, check connections before activation, and improve workflows that process large numbers of items.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.
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Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).
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Use this skill when a company needs its record of processing activities for the first time, or has one that is really a list of tools, and asks "build our RoPA", "records of processing", "data map for GDPR", "Article 30 record", "what personal data do we process and why", "data inventory for privacy" or "our DPO needs a processing register". Drafts the record by processing activity from systems, vendors and team interviews, with purposes, data and subject categories, proposed legal bases, recipients, transfers and concrete retention, plus a separate processor record when the company processes data for customers, and a notes file of open questions per owner.
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Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan GDPR data handling for systems processing EU personal data. Review consent, requests from people to access or delete their data, collection limits, retention, encryption, and access controls as part of system design and compliance reviews.
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Turn a business use case into a Dataverse solution using Python. Get table and relationship designs, implementation code, usage instructions, and recommendations for error recovery, logging, monitoring, and performance based on the expected workload.
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Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.
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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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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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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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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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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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Creates GDPR Article 30(1) Records of Processing Activities (RoPA) for data controllers with all seven mandatory fields: controller identity and contact details, processing purposes, data subject categories, personal data categories, recipient categories, third country transfers, and retention periods. Includes Python generator for automated RoPA creation. Activate for controller RoPA, Art. 30(1), processing records, data mapping.
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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
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Check task processing statistics to see how much data and how many docs are moving. Use when checking throughput, diagnosing a stalled task, verifying data movement, or reporting on task activity. Use when user says "is data flowing", "how much data", "check throughput", "how many documents", "task not processing", "data not arriving", "stats", "bytes processed", "stalled capture", "no data in destination", "data volume", "task report", "usage report", or "how much data did we process".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build or review Java Spring Boot backends using examples for web APIs, separate application layers, and database access. Covers input validation, error handling, caching, background processing, and logging to guide maintainable implementations.
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Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.
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Design dbt data transformations with clearly organized models, data quality tests, documentation, and incremental processing that updates only part of a dataset. Use it to structure analytics tables and organize a dbt project.
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Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.
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Guides implementation of audience uploads, individual removals, and full replacements through the Data Manager API. It covers formatting and hashing identifiers, validating requests, and checking processing results for Customer Match and supported device ID audiences.
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