Results for “dbt-core”

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis.

Bitbucket CLI for Data Center and Cloud. Use when users need to manage repositories, pull requests, branches, issues, webhooks, or pipelines in Bitbucket. Triggers include "bitbucket", "bkt", "pull request", "PR", "repo list", "branch create", "Bitbucket Data Center", "Bitbucket Cloud", "keyring timeout".

Bitbucket CLI for Data Center and Cloud. Use when users need to manage repositories, pull requests, branches, issues, webhooks, or pipelines in Bitbucket. Triggers include "bitbucket", "bkt", "pull request", "PR", "repo list", "branch create", "Bitbucket Data Center", "Bitbucket Cloud", "keyring timeout".

Build .NET 8 applications with minimal APIs, clean architecture, CQRS, Entity Framework Core, and JWT authentication.

Analyzes core dumps and live processes with GDB, correlating runtime state with source code to diagnose crashes, deadlocks, and memory issues.

etcd distributed key-value store reference. Backbone of Kubernetes. Covers key-value operations, watch for real-time updates, leases with TTL, atomic transactions, cluster setup, backup/restore, authentication, TLS, and Prometheus monitoring.

Updates the dbskill collection from the official repository when the user requests an update, upgrade, or check for updates.

Wire ETL, ingestion, cron, edge-function, and queue jobs correctly. Use for "build a pipeline", "sync X into Y", "nightly aggregation", "cron double-counts", "dedupe", "backfill", "the numbers are wrong after a retry". Bakes in idempotency, atomic writes, data contracts, dead-letter, and observability.

Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).