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Found 7 Skills
Create Data Contracts (CTR) - Optional Layer 8 artifact using dual-file format (.md + .yaml) for API/data contracts
Use when defining events, fields, and governance for GTM analytics pipelines.
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.
Use when you need to turn selected modules (P0 priority first) into single-page module SSOT at the path `.aisdlc/project/components/{module}.md`, and build authoritative entries for API/Data contracts, invariant summaries, evidence entries and structured Evidence Gaps in the same page to meet the DoD gate requirements of Discover.
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
Data quality framework covering completeness, accuracy, consistency, validation rules, and data contracts. Use when implementing data validation, setting up data quality checks, or defining data contracts.