Data product design patterns with contracts, SLAs, and governance for building self-serve data platforms using Data Mesh principles.
Profile datasets to understand schema, quality, and characteristics. Use when analyzing data files (CSV, JSON, Parquet), discovering dataset properties, assessing data quality, or…
Generate comprehensive data profiles for DataFrames. Use for EDA, data discovery, and understanding dataset characteristics.
GDPR compliance analysis covering lawful basis assessment, privacy notices, processor agreements, and breach response.
Prüft AVV/DPA, Rollen, Subprozessoren, TOMs, Drittlandtransfer, Telemetrie und Produktdaten.
Pre-flight personal-data and GDPR check for conveyancing AI use. Run at the START of any conversation and whenever the user pastes or uploads content, BEFORE answering — scan for…
Data Protection Transfer DD für M&A: prüft DSGVO-Risiken, Datenuebertragung, Datenraumzugriff, Kunden-/Mitarbeiterdaten, Drittlandtransfer und Post-Closing-Migration: Data…
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an…
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
Enforce data quality rules and validations on pilot data streams and repositories. Use when checking for missing values, schema compliance, consistency issues, or anomalies before…
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or esta — from…
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or esta — from…
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or esta — from…
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or esta — from…
Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly…
Techniques and tools for ensuring the accuracy, completeness, and reliability of data across the pipeline.
Design or audit a recurring Data Reliability Contract for data products and pipelines—datasets, streams, imports, transformations, warehouses, and search/operational…
Profiles data assets to assess quality dimensions, detect anomalies, and generate comprehensive data quality reports with actionable recommendations.
See the main Data Validation Rules skill for comprehensive coverage of data quality rule implementation.
Write and verify SQL queries with BigQuery. Use when executing bq commands, writing SQL queries, or including query results in documents.
Reconcile any two record sets (A vs B) and triage the discrepancies — match line-by-line on a shared key, or heuristically on amount + date, then classify every unreconciled item…
Derives a missing workbook cell from a fully determined constraint: row/column remainder, YoY invert, share x total, CAGR endpoints, weighted-average sleeve, then…
Reconciles data sources using stable identifiers (Pay Number, driving licence, driver card, and driver qualification card numbers), producing exception reports and “no silent…
Patterns for reconciling Salesforce data with external systems: count-level, field-level, and record-level reconciliation, external ID upsert patterns, Change Data Capture for…
Build and refresh eval datasets from Front, run routing evals, and analyze agent response quality.
Routes an incoming consumer data request to the team that owns it, using the request type and the requester's state of residence, and reports the owner, the queue, and the…
Implement retention tiers, legal holds, and deletion pipelines that actually delete, including GDPR-style erasure.
Datenraum für Board/Gremien: steuert Gremienunterlagen, Rechte, Versionen, Beschlüsse und Kanzleiinput sicher teilen zwischen Kanzlei, Mandant und Rechtsabteilung mit Dashboard,…
Für Datenraum-Index VC: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.
Build and maintain the investor data room — organize financials, metrics, legal docs, contracts, and customer references for fundraising due diligence.
Layer C of spectra pre-handoff quality gates. Static audit for the data-shape failure class where a client query param literal violates the server zod schema bound (e.g.
Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system…
TradingView 데이터 스크래핑 자동화 파이프라인 가이드. 사용 시점: (1) 스크래퍼 실행 방법 문의 시, (2) 데이터 파이프라인 아키텍처 이해 필요 시, (3) DB 업로드 설정/디버깅 시, (4) 스크래핑 자동화 확장 작업 시.
数据语义服务 API - 提供表单视图的语义理解功能。 用于: (1) 查询字段语义和业务对象识别结果 (2) 触发/批量理解表单视图 (3) 批量业务对象匹配
Разделение данных 1С. Используй когда нужно проверить tenant-boundaries, разделители, безопасные запросы, RLS/права, фоновые задания, обмены или интеграции в разделенной базе.
Efficient data serialization for game networking including Protobuf, FlatBuffers, and custom binary
Use when you have a new or draft file format specification (e.g., mzPeak) with multiple independent language implementations, and you need to verify that all readers agree on the…
Create data fetching services with circuit breaker pattern for API resilience. Services handle fetch, cache, retry, and expose typed data to panel components.
Create data fetching services with circuit breaker pattern for API resilience. Services handle fetch, cache, retry, and expose typed data to panel components.
Create data fetching services with circuit breaker pattern for API resilience, including a first-class pattern for building optional query parameters via URLSearchParams.
Create resilient data fetching services with circuit breaker pattern, supporting both proxied and direct API calls.
Detect and map data silos in construction organizations. Identify disconnected data sources and integration opportunities
Diagnose and mitigate Salesforce data skew — ownership skew (single user owns >10,000 records) and parent-child skew (>10,000 children under one parent) — that cause sharing…
본 스킬은 대한민국 공공데이터포털(https://www.data.go.kr/)에서 제공하는 각종 공공데이터 API 를 사용하기 위한 설명입니다. 본 스킬은 공공데이터 개발 또는 공공 API 개발을 할 때 사용하면 됩니다.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques.
Connect your own data source to replace the demo unicorns data. Use when the user wants to use their own database URL or CSV file instead of the sample data.
評估新數據源的可行性、整合難度和預期 alpha 貢獻。標準化 Spike 框架： 目標定義 → API 文件搜尋 → 技術可行性 → 整合架構 → 成本分析 → 建議評級。 適用於評估新 API、替代數據源、競品數據、政府公開資料。 觸發詞: 評估數據源, evaluate data source, 新 API, 可行性, feasibility,…
Optimize provider selection, routing, and credit usage across 150+ enrichment sources for company/contact intelligence. — from general/general-misc
Optimize provider selection, routing, and credit usage across 150+ enrichment sources for company/contact intelligence. — from general/general-misc
Navigate cross-border data transfer rules — Schrems II, EU SCCs, adequacy decisions, data localization requirements for EU, US, Brazil, and other jurisdictions.
Requêtes SQL avancées pour l'analytique — window functions, CTEs récursives, pivots et optimisation de requêtes complexes.
Use when reviewing or creating SQL for analysis, metric definitions, joins, filters, aggregation, and validation queries.
Master data sqlmesh with comprehensive coverage of concepts, implementation, optimization, and production best practices. Essential skill for professionals working in data.
Design streaming data pipelines — Kafka, Kinesis, Pub/Sub, Flink — with event schemas, stream processing, and real-time analytics.
Recommend basic data structures for a task. Use when a junior developer needs help choosing lists, maps, or sets.
Use when you have raw GC-MS output in CSV format with columns Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, and File.
Give agents persistent structural memory of a codebase — navigate dependencies, track public APIs, and understand why connections exist without re-reading the whole repo.
Choose the structure whose operations match how the data will actually be used, rather than defaulting to a list or a map.
Use when after converting or filtering objects (e.g., transformation products to suspect lists, feature groups through componentization) and before passing them to downstream…