Use when planning how to archive aging Salesforce records to reduce storage costs, maintain query performance, and meet retention policies.
Using data-* attributes as the HTML/CSS/JS bridge for state, variants, and configuration. Use when managing element state, styling variants, or configuring behavior without…
Update data-cleaning-implementation skill with critical pandas patterns and testing learnings from vp-e62a
Use when designing or evaluating a Data Cloud implementation architecture — covering data lake strategy (DSO/DLO/DMO layers), identity resolution rule design, activation target…
Use when choosing or explaining how Salesforce Data Cloud (Data 360) and CRM Analytics (Tableau CRM / Einstein Analytics) fit together versus overlap — unified data platform vs…
Investigate mismatches, duplicates, missing records, broken keys, and reconciliation defects using structured SQL or Python diagnostics.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
Operate as a data engineer who ships pipelines to a freshness SLA, enforces schema contracts at the source, and owns data quality end to end.
Use when user needs scalable data pipeline development, ETL/ELT implementation, or data infrastructure design.
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter — from majiayu000/claude-skill-registry
Design an end-to-end B2B data enrichment architecture — provider selection, waterfall design, cost modeling, and CRM hygiene.
Common patterns for extracting and combining analytics data from GA4, GSC, and SE Ranking. Includes API patterns, rate limiting, caching, and error handling.
Best practices and conventions for server-side data fetching, caching, and rendering in Next.js 16+ applications.
LobeHub data-fetching pipeline guide. Use for service layer, Zustand store, SWR, lambdaClient, useClientDataSWR, useFetchXxx hooks, or migrating useEffect fetches.
Architect data fetching with TanStack Query v5, SWR, optimistic updates, prefetching, and cache invalidation strategies.
Use Python to profile exports, find data quality issues, inspect bad records, and summarize findings in plain language.
Skill professionnel pour Claude Code permettant d'accéder, télécharger et analyser les données ouvertes françaises via data.gouv.fr.
Use when planning, reviewing, or troubleshooting Salesforce data imports, migrations, and bulk updates.
Use when planning a product feature, fix, or prioritization task where Pendo usage data, signals, issues, or customer feedback could ground the plan in real product behavior —…
Shared infrastructure setup for data migration and data validation — prerequisites, compute pool, worker config, and source database/schema capture.
Build data ingestion pipelines for batch and streaming data from multiple sources. Covers extraction strategies, format normalization, deduplication, validation gates, and staging…
Use this agent when you need to review database migrations, data models, or any code that manipulates persistent data.
Verifying data integrity constraints, referential integrity, data type validation, and consistency checks across database operations.
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Patterns Apache Kafka — topics, partitions, consumer groups, exactly-once semantics et Kafka Streams.
Implements data classification labels and tagging systems including metadata tagging, DLP integration, automated label propagation, user-applied labels, and label inheritance…
Provides architectural guidance for data lake design including partitioning strategies, storage layout, schema design, and lakehouse patterns.
Data Lake architecture and management including medallion architecture (bronze/silver/gold zones), data catalog with AWS Glue, partitioning strategies, schema evolution, data…
Data Mesh architecture patterns — domain ownership, data products with SLOs, self-serve platform design, Delta Lake vs Iceberg, federated Trino queries, data contracts,…
Use when planning, reviewing, or troubleshooting a Salesforce data migration — covering tool selection (Data Loader, Bulk API 2.0, MuleSoft, Informatica, Jitterbit), migration…
Create safe, reversible database migration scripts with rollback capabilities, data validation, and zero-downtime deployments.
Analyze and Refactor TypeScript data models. Detect duplication, improve type safety, and validate relationships in interfaces/types.
Database schema design and data modeling patterns including normalization principles (1NF-5NF), denormalization trade-offs, entity relationship design, indexing strategies, schema…
Enforce consistent structure, formatting, and placement for static data objects (catalogs, config, content, lookup tables, enums-as-data) in TypeScript/JavaScript codebases.
Trigger: database schema, indexing, cache asidePattern, Redis caching, ORM models, Drizzle schema. Scope: Database query tuning, indexing, and data cache designs.
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements.
트리거: "데이터 파이프라인", "celery task", "kafka consumer", "파이프라인 만들어줘", "비동기 작업", "airflow dag", "airflow 만들어줘", "배치 파이프라인", "rabbitmq", "메시지 큐", "etl 파이프라인", "데이터 처리", "스케줄러 만들어줘" 수행:…
Review or design a data pipeline architecture. Assesses ingestion pattern, transformation design, orchestration, idempotency, freshness SLAs, data contracts at boundaries, dbt…
Testing data pipelines including ETL validation, data quality checks, pipeline orchestration testing, and data lineage verification.
Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s…
Comprehensive guide to data quality validation, testing frameworks, anomaly detection, and data observability for production data pipelines
中文优先：用于数据抓取智能体相关任务，帮助识别、设计、实现或验证对应工作流。English keywords: Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports,…
Create database seed scripts with realistic test data for development and testing. Use when setting up development environment or creating demo data.
Generates deterministic seed data for development and testing with factory functions, realistic fixtures, and database reset scripts.
Use when creating test data for scratch orgs, sandboxes, or CI pipelines: Apex @testSetup factories, sf data import tree plans, CumulusCI datasets, Snowfakery.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.).
Guide Blumark24 OS data storage decisions, tenant-bound data, sensitive-data handling, and avoiding localStorage for business data.
Salesforce storage management: monitoring data storage vs file storage usage, diagnosing storage alerts, optimizing large text fields, choosing between Attachment and…
Python data structure conventions for this codebase. Apply when choosing between Pydantic models, dataclasses, and other data containers.
Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing…
Use when studying, explaining, designing, reviewing, or implementing data systems concepts including stream processing, replication, partitioning, transactions, isolation,…
Create Next.js data table pages with SSR initial load, SWR caching, and server-response-based UI updates.
Data-to-UI pipeline patterns. Use when transforming JSON data into React components, creating TypeScript types from schemas, building derived types, or creating data utilities.
Validation et qualité des données dans les pipelines. Se déclenche avec "data quality", "validation des données", "data contract", "schema validation", "Great Expectation — from…
建立「互動式資料視覺化應用」的完整工作流程 skill。從一份資料（CSV/JSON/GeoJSON）與需求出發， 走完 規格釐清 → 資料處理 → 圖表選型 → 元件實作 → 多層次驗證，產出「可運作、能互動、數值正確、 風格一致」的 React + Tailwind + Chart.js/D3 前端。核心是「內容與樣式分離」兩階段設計，並以實際 執行…
State-of-the-art data visualization for React/Next.js/TypeScript with Tailwind CSS. Creates compelling, tested, and accessible visualizations following Tufte principles and NYT…
트리거: "차트 만들어줘", "데이터 시각화", "그래프", "차트 코드", "시각화해줘", "plotly", "matplotlib", "seaborn", "d3", "python 시각화", "대시보드 차트" 수행: 데이터셋 구조 및 프레임워크 분석 → 최적 차트 유형 선택 → Chart.js /…