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Found 74 Skills
Pro tips for B2B list building - source mixing, enrichment workflow, template usage, and efficiency principles. Use when building prospect lists, optimizing data quality, or improving prospecting efficiency.
Guides cleaning and standardizing tabular datasets before analysis, modeling, or reporting—profiling, quality rules, missing values, duplicates, outliers, type coercion, encoding fixes, record linkage, deduplication, high-level PII handling (not legal advice), actuarial/insurance field scrubbing, reproducible scrub pipelines, validation checks, and sign-off. Distinct from warehouse ETL or statistical modeling. Use when the user asks for "data scrubbing", "clean this dataset", "scrub the data", "data cleaning", "dedupe records", "handle missing values", "outlier treatment", "standardize columns", "data quality rules", "profile this table", or "prepare data for modeling". Not warehouse pipelines (data-warehouse-engineer), ML modeling (data-scientist, actuary), privacy programs (compliance-engineer), FinOps only (finops-analyst), or assumption governance (assumption-setting).
SQL for data analysis with exploratory analysis, advanced aggregations, statistical functions, outlier detection, and business insights. 50+ real-world analytics queries.
Profile a new tabular dataset before modeling. Find target leakage, missing data patterns, high-cardinality categoricals, near-constant features, redundant pairs, and non-linear relationships that Pearson correlation misses. Use whenever the user hands you a CSV or parquet and asks "what should I do with this?" Always run this skill before training any model on data you haven't seen before.
Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.
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
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
Melissa Data integration. Manage data, records, and automate workflows. Use when the user wants to interact with Melissa Data data.