data-engineering
113
market-microstructure
DEX orderflow analysis, trade classification, buyer/seller pressure, and microstructure signals for Solana tokens
NeverSight
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
data-engineering
113
shredstream
Pre-execution Solana transaction streaming via Jito ShredStream, Shyft RabbitStream, and Triton Deshred
NeverSight
data-ai
data-engineering
113
dolos-pipeline
Architecture of the Dolos processing pipeline — WorkUnit lifecycle, executor modes, CardanoWorkUnit variants, WorkBuffer state machine, and sequencing. Reference when debugging execution ordering, understanding phase boundaries, or adding new work unit types.
txpipe
data-ai
data-engineering
113
deeplake-managed
SDK for ingesting data into Deeplake managed tables. Use when users want to store, ingest, or query data in Deeplake.
NeverSight
data-ai
data-engineering
113
web-state-redux-toolkit
Redux Toolkit patterns for complex client state. Use when managing enterprise-scale state, needing DevTools, entity normalization, or RTK Query for data fetching.
NeverSight
data-ai
data-engineering
113
oracle-dba
Use when managing Oracle Autonomous Database on OCI, troubleshooting performance, optimizing costs, or implementing HA/DR. ADB-specific gotchas, cost traps, SQL_ID debugging workflows, auto-scaling behavior, and version differences (19c/21c/23ai/26ai). Keywords: ADB, Autonomous Database, ECPU, auto-scaling, SQL_ID, wait events, ORA- errors, wallet, BYOL.
NeverSight
data-ai
data-engineering
113
web-data-fetching-graphql-apollo
Apollo Client GraphQL patterns - useQuery, useMutation, cache management, optimistic updates, subscriptions
NeverSight
data-ai
data-engineering
113
analytics-engineering
Use this skill when building dbt models, designing semantic layers, defining metrics, creating self-serve analytics, or structuring a data warehouse for analyst consumption. Triggers on dbt project setup, model layering (staging, intermediate, marts), ref() and source() usage, YAML schema definitions, metrics definitions, semantic layer configuration, dimensional modeling, slowly changing dimensions, data testing, and any task requiring analytics engineering best practices.
NeverSight
data-ai
data-engineering
113
real-time-streaming
Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.
NeverSight
data-ai
data-engineering
113
data-pipelines
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.
NeverSight
data-ai
data-engineering
113
data-warehousing
Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
NeverSight
data-ai
data-engineering
113
data-quality
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.
NeverSight
data-ai
data-engineering
113
deploy
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
databricks
data-ai
data-engineering
113
deploy
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
databricks
data-ai
data-engineering
113
deploy
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
databricks
data-ai
data-engineering
113
deploy
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
databricks
data-ai
data-engineering
113
deploy
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
databricks
data-ai
data-engineering
109
building-with-kafka-strimzi
Use when building event-driven systems with Apache Kafka on Kubernetes. Triggers include EDA patterns, Kafka producers/consumers, Strimzi operator deployment, Schema Registry, transactions, exactly-once semantics. NOT for general messaging (use Dapr pub/sub for abstraction).
panaversity
data-ai
data-engineering
90
projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
aiskillstore
data-ai
data-engineering
90
data-quality-frameworks
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.
aiskillstore
data-ai
data-engineering
90
wap-ingestion
Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.
aiskillstore
data-ai