Loading...
Loading...
Found 56 Skills
Use this skill when users need to create, modify, or validate Salesforce Validation Rules. Trigger when users mention validation rules, field validation, data quality rules, formula validation, error messages, or validation logic. Also use when users encounter validation errors, need to update formulas, or want to enforce business rules at the data layer. Always use this skill for any validation rule work.
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
Use this skill when users need to create, modify, or validate Salesforce Validation Rules. Trigger when users mention validation rules, field validation, data quality rules, formula validation, error messages, or validation logic. Also use when users encounter validation errors, need to update formulas, or want to enforce business rules at the data layer. Always use this skill for any validation rule work.
Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
pytest, data validation, Great Expectations, and quality assurance for data systems
Melissa Data integration. Manage data, records, and automate workflows. Use when the user wants to interact with Melissa Data data.
Validate the column contract of a newly written table — column set, types, and nullability match expectations. Object existence and row counts are handled by the builtin layer and are out of scope. Data-content assertions belong to project-level validator skills.
Principal backend engineering intelligence for Python AI/ML systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale ML services and pipelines. Focus: data quality, reproducibility, reliability, performance, security, observability, model evaluation, MLOps.
Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
End-to-end data engineering pipeline using MinIO, Airbyte, PostgreSQL, DBT, and Airflow with medallion architecture (Bronze/Silver/Gold layers)
Use this skill when the user wants to manage data quality in DataHub: create or run assertions, check assertion outcomes, raise or resolve incidents, create notification subscriptions, or diagnose health problems across their estate. Triggers on: "create assertion", "run assertion", "check quality", "data quality", "health check", "raise incident", "resolve incident", "subscribe to", "failing assertions", "active incidents", or any request involving data quality, assertions, incidents, or quality notifications.