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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.
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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.
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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).
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Data Quality Specialist IA — Expert en qualité des données (profiling, cleaning, déduplication, validation de schéma, Great Expectations)
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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).
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Review prediction-market and trading-agent workflows before they handle account access, portfolio data, or trade planning. Produces structured findings on venue rules, data quality, security, privacy, and execution risks, with required safeguards and a safe next step.
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Build data quality checks with Great Expectations, dbt tests, and data contracts, which define shared expectations between teams. Check missing values, duplicates, and freshness, then integrate validation into data pipelines and CI/CD.
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Assess whether data is trustworthy enough for analysis, dashboards, experiments, or modeling. Check missing values, duplicates, consistency, freshness, and changes over time, then explain the evidence, downstream risks, likely causes, and practical fixes.
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Review reports, spreadsheets, dashboards, or other analyses before sharing them or making decisions. Check methods, calculations, charts, and conclusions, then receive a readiness assessment with prioritized fixes, supporting evidence, and limitations that readers need to know.
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Create, edit, or validate Jupyter notebooks for SQL and Python analysis. Organize data sources, assumptions, calculations, and findings into a readable document that others can review and rerun, with execution gaps clearly identified.
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Design key performance indicators for business or product decisions, including definitions, calculations, targets, and measurement plans. Select supporting metrics that explain progress and guardrails that track unwanted effects on quality, cost, or other outcomes.
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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.
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[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.
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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.
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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.
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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.
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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.
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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.
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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.
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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。
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Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
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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.
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Routes AutoViz users to automated EDA, data-quality, and text/wordcloud workflows.
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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.
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