data-engineering
31
apache-spark-data-processing
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
manutej
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
data-engineering
31
sparql-university
Guidance for writing SPARQL queries against RDF/Turtle datasets, particularly for university or academic data. This skill should be used when tasks involve querying RDF data with SPARQL, filtering entities based on multiple criteria, aggregating results, or working with Turtle (.ttl) files.
letta-ai
data-ai
data-engineering
31
sparql-university
Guidance for writing and verifying SPARQL queries against RDF datasets, particularly university/academic ontologies. This skill should be used when tasks involve querying RDF data with SPARQL, working with academic datasets (students, professors, departments, courses), or performing complex graph pattern matching with filters and aggregations.
letta-ai
data-ai
data-engineering
31
kafka-stream-processing
Complete guide for Apache Kafka stream processing including producers, consumers, Kafka Streams, connectors, schema registry, and production deployment
manutej
data-ai
data-engineering
31
multi-source-data-merger
This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.
letta-ai
data-ai
data-engineering
31
reshard-c4-data
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
letta-ai
data-ai
data-engineering
31
dbt-data-transformation
Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows
manutej
data-ai
data-engineering
29
manage-seeders
Manages Database Seeders with advanced support for JSON data sources, idempotency checks, and relationship mapping.
iurygdeoliveira
data-ai
data-engineering
26
tanstack-query-advanced
Advanced TanStack Query v5 patterns for infinite queries, optimistic updates, prefetching, gcTime, and queryOptions
yonatangross
data-ai
data-engineering
26
golden-dataset-management
Use when backing up, restoring, or validating golden datasets. Prevents data loss and ensures test data integrity for AI/ML evaluation systems.
yonatangross
data-ai
data-engineering
26
golden-dataset-validation
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
yonatangross
data-ai
data-engineering
26
gcs-data-catalog
Activates when querying Danish agricultural data from GCS. Use this skill for: data discovery, finding datasets, understanding schemas, querying parquet files, joining datasets on CVR/CHR/BFE identifiers. Keywords: data, catalog, datasets, GCS, parquet, schema, query, DuckDB, pyarrow
Klimabevaegelsen
data-ai
data-engineering
25
streamlit-development
Developing, testing, and deploying Streamlit data applications on Snowflake. Use this skill when you're building interactive data apps, setting up local development environments, testing with pytest or Playwright, or deploying apps to Snowflake using Streamlit in Snowflake.
sfc-gh-dflippo
data-ai
data-engineering
25
dbt-core
Managing dbt-core locally - installation, configuration, project setup, package management, troubleshooting, and development workflow. Use this skill for all aspects of local dbt-core development including non-interactive scripts for environment setup with conda or venv, and comprehensive configuration templates for profiles.yml and dbt_project.yml.
sfc-gh-dflippo
data-ai
data-engineering
25
snowflake-connections
Configuring Snowflake connections using connections.toml (for Snowflake CLI, Streamlit, Snowpark) or profiles.yml (for dbt) with multiple authentication methods (SSO, key pair, username/password, OAuth), managing multiple environments, and overriding settings with environment variables. Use this skill when setting up Snowflake CLI, Streamlit apps, dbt, or any tool requiring Snowflake authentication and connection management.
sfc-gh-dflippo
data-ai
data-engineering
24
exploratory-data-analysis
EDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights.
lifangda
data-ai
data-engineering
24
file-processing
Process and analyze CSV, JSON, and text files with data transformation, cleaning, analysis, and visualization capabilities
aws-samples
data-ai
data-engineering
24
data-lake-platform
Universal data lake and lakehouse patterns covering ingestion (dlt, Airbyte), transformation (SQLMesh, dbt), storage formats (Iceberg, Delta, Hudi, Parquet), query engines (ClickHouse, DuckDB, Doris, StarRocks), streaming (Kafka, Flink), orchestration (Dagster, Airflow, Prefect), and visualization (Metabase, Superset, Grafana). Self-hosted and cloud options.
vasilyu1983
data-ai
data-engineering
24
data-engineer
Expert data engineer specializing in building scalable data pipelines, ETL/ELT processes, and data infrastructure. Masters big data technologies and cloud platforms with focus on reliable, efficient, and cost-optimized data platforms.
zenobi-us
data-ai
data-engineering
24
ai-ml-data-science
End-to-end data science patterns (modern best practices): problem framing -> data -> EDA -> feature engineering (with feature stores) -> modelling -> evaluation -> reporting, plus SQL transformation (SQLMesh). Emphasizes MLOps integration, drift monitoring, and production-ready workflows.
vasilyu1983
data-ai
data-engineering
24
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
lifangda
data-ai
data-engineering
23
managing-bd-tasks
Use for advanced bd operations - splitting tasks mid-flight, merging duplicates, changing dependencies, archiving epics, querying metrics, cross-epic dependencies
withzombies
data-ai