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Automate Hugo-based blog post creation for dabase.com with SEO-friendly metadata, dynamic date handling, and filename generation.
Curated agent skills for data quality — compare tools, clone hosted repos, and install with Skill Manager.
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13 skills found
Automate Hugo-based blog post creation for dabase.com with SEO-friendly metadata, dynamic date handling, and filename generation.
Analyze A/B test results with statistical rigor. Includes significance testing, sample size validation, guardrail monitoring, and actionable recommendations (ship/extend/stop) using Python scripts.
Expert guidance for Neo4j Cypher queries and MCP server tools, focusing on schema introspection, graph operations, and efficient database development workflows.
A versatile data analysis assistant for loading datasets, performing statistical calculations, visualizing trends, and generating professional summary reports.
Automated single-cell RNA-seq quality control pipeline following scverse best practices. Performs MAD-based outlier detection, cell filtering, and diagnostic visualization for .h5ad and .h5 datasets.
Specialized data engineering agent for designing ETL/ELT pipelines, defining data schemas, managing data quality, and implementing robust ingestion workflows.
Create, alter, and validate Snowflake semantic views via the CLI. Automate the generation, documentation, and testing of semantic layer definitions to ensure model accuracy and star schema compliance.
Generates data cleaning pipelines for pandas/polars/PySpark, handling missing values, duplicates, outliers, type conversions, and validation.
World-class senior data engineering skill for building scalable data pipelines, ETL/ELT systems, and modern data infrastructure using Python, Spark, dbt, and Kafka.
Guidelines for curating high-quality datasets for LLM post-training (SFT/DPO/RLHF), covering data formats, quality filtering, and collection strategies.
Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.
Implement production-grade data quality validation using Great Expectations, dbt tests, and data contracts to ensure reliable pipelines.