43 件 ・ 関連度順
概要と使いどころ
Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.
日本語の概要は準備中です。原文の説明を表示しています。
Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
日本語の概要は準備中です。原文の説明を表示しています。
Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).
日本語の概要は準備中です。原文の説明を表示しています。
Data Quality Specialist IA — Expert en qualité des données (profiling, cleaning, déduplication, validation de schéma, Great Expectations)
日本語の概要は準備中です。原文の説明を表示しています。
Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).
日本語の概要は準備中です。原文の説明を表示しています。
予測市場や取引エージェントの処理を、規約・データ品質・秘密情報・プライバシー・取引実行の観点で点検し、問題点と必要な対策を整理するスキル。
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
日本語の概要は準備中です。原文の説明を表示しています。
Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.
日本語の概要は準備中です。原文の説明を表示しています。
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
日本語の概要は準備中です。原文の説明を表示しています。
[omh] Data pipeline work -- an ETL or streaming job, a backfill or replay, duplicate events, a schema change downstream, a lineage question, a data-quality regression: make every rerun idempotent, bound every replay, and gate each load on observed checks. Use when the user says: data-pipelines, data pipeline, data pipelines, etl, elt, etl pipeline, etl job, etl backfill.
日本語の概要は準備中です。原文の説明を表示しています。
Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.
日本語の概要は準備中です。原文の説明を表示しています。
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
日本語の概要は準備中です。原文の説明を表示しています。
Sanity-check a greenhouse gas inventory before it goes into a report or gets audited. Use when asked to review a carbon footprint, check a GHG inventory, validate scope 1/2/3 numbers, explain a year-over-year emissions change, or prepare emissions data for assurance. Produces a boundary review, data-quality assessment, emission-factor sensitivity list, YoY bridge, and a fix-before-publishing list.
日本語の概要は準備中です。原文の説明を表示しています。
Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.
日本語の概要は準備中です。原文の説明を表示しています。
ByteHouse 数据质量检查工具。当用户提供集群连接信息、数据库名和表名，需要检查排序键、主键和分区键所使用的列的空值、零值情况，是否存在异常分布，以及排序键、主键的重复情况时，使用此技能。适用于 "数据质量检查"、"排序键 / 主键 / 分区键分析"、"空值分析"、"零值分析"、"重复键检查"、"Top 分布" 等诉求。
日本語の概要は準備中です。原文の説明を表示しています。
For any completed deliverable, enforce a strict multi-round subAgent review-and-revise loop via sessions_spawn. Invoke before final output or whenever high-standard quality gating is required.
日本語の概要は準備中です。原文の説明を表示しています。
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.
日本語の概要は準備中です。原文の説明を表示しています。
Routes AutoViz users to automated EDA, data-quality, and text/wordcloud workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Processes GDPR Article 16 right to rectification requests, covering verification of corrected data accuracy, notification to recipients under Article 19, timeline management, and completion of incomplete data. Activate for rectification, correction request, inaccurate data, Art. 16, data correction queries.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Validate data quality in CSV, JSON, and database exports by checking for missing values, type mismatches, duplicates, outliers, and schema violations. Use when building ETL pipelines, auditing data imports, checking data freshness, or ensuring data contracts between teams. Trigger words: data quality, validation, null values, duplicates, schema check, data contract, ETL, pipeline, data drift.
日本語の概要は準備中です。原文の説明を表示しています。
Use for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.
日本語の概要は準備中です。原文の説明を表示しています。