de-agent-skills

In the author's own words, from each skill's SKILL.md — showing the first 30 of 159 files.

• airbyte

  Airbyte ELT — source/destination connectors, sync modes (Full Refresh Overwrite/Append, Incremental Append/Deduped), cursor fields, primary keys, catalog/streams, deployment (abctl, Kubernetes Helm, Airbyte Cloud), Connector Builder, Python CDK (HttpStream, IncrementalMixin), normalization (dbt-based, _airbyte_raw_ tables), Airbyte API, Terraform provider, schema evolution, monitoring, Airflow Ai…

  skills/airbyte/SKILL.md on GitHub (opens in a new tab)

• airflow_dag_factory

  Use when building, reviewing, debugging, or scaling Apache Airflow DAGs declaratively with dag-factory YAML configs — including project setup, loader configuration, defaults hierarchy, custom/provider operators, dynamic task mapping, datasets/assets, callbacks, TaskFlow decorators, Jinja2 templating, environment variables, YAML DRY patterns, large-scale multi-DAG generation, CI/CD linting, and mi…

  skills/airflow_dag_factory/SKILL.md on GitHub (opens in a new tab)

• airflow-dags

  Apache Airflow DAG authoring — DAG definition, TaskFlow API (@task/@dag decorators), operators (Bash/Python/SQL/HTTP), sensors (poke/reschedule modes), TaskGroups, dynamic task mapping (expand/partial), branching, trigger rules, XComs, Pools, callbacks, cross-DAG pipelines, best practices

  skills/airflow_dags/SKILL.md on GitHub (opens in a new tab)

• apache-flink

  Apache Flink — streaming architecture (JobManager/TaskManager/slots/parallelism), PyFlink Table API and SQL (Kafka/filesystem DDL, windowing TUMBLE/HOP/SESSION, aggregations), DataStream API (KeyedStream, stateful operations ValueState/MapState, process functions), event-time vs processing-time, watermark strategies, checkpointing (exactly-once, RocksDB state backend), Kafka source/sink (delivery…

  skills/apache_flink/SKILL.md on GitHub (opens in a new tab)

• apache-kafka

  Apache Kafka — topics, partitions, consumer groups, producer/consumer configuration (acks, idempotence, compression, commit strategies), exactly-once semantics, Kafka Connect (source/sink connectors, SMTs, DLQ), Schema Registry, consumer lag monitoring, CLI operations, Python confluent-kafka examples, partition strategy, replication, retention

  skills/apache_kafka/SKILL.md on GitHub (opens in a new tab)

• cdc-debezium

  Change Data Capture with Debezium — PostgreSQL/MySQL/Oracle CDC connectors, change event structure (before/after/op/source), snapshot modes, Kafka Connect deployment, SMT transformations, outbox pattern, Iceberg/Delta sink integration, idempotency guarantees

  skills/cdc_debezium/SKILL.md on GitHub (opens in a new tab)

• clickhouse-olap

  ClickHouse OLAP — MergeTree family engines (MergeTree/ReplacingMergeTree/AggregatingMergeTree/SummingMergeTree/CollapsingMergeTree), ORDER BY/PARTITION BY/PRIMARY KEY design, data skipping indexes (minmax/set/bloom_filter), TTL tiered storage, materialized views, projections, LowCardinality/Nullable/AggregateFunction types, INSERT batching, FINAL keyword, query optimization (PREWHERE/parallel_rep…

  skills/clickhouse_olap/SKILL.md on GitHub (opens in a new tab)

• dagster-assets

  Dagster Software-Defined Assets (SDA) — @asset/@multi_asset decorators, asset dependencies, partitions (daily/static/dynamic/multi), sensors, IO managers, declarative automation (AutomationCondition), jobs, schedules, resource definitions, deployment

  skills/dagster_assets/SKILL.md on GitHub (opens in a new tab)

• data-contracts

  Data Contracts — datacontract.com YAML specification (schema, quality, SLA, ownership, servers, changelog), Data Contract CLI (init/test/lint/diff/breaking/publish/export), SodaCL-style quality checks embedded in contract, breaking change detection (column removal/type change/constraint tightening), CI/CD GitHub Actions PR enforcement, Kafka/S3/database server definitions, contract-driven develop…

  skills/data_contracts/SKILL.md on GitHub (opens in a new tab)

• data_vault_2

  Use when designing or implementing Data Vault 2.0 data models — Hubs, Links, Satellites, Reference Tables, Same-As Links, Multi-Active Satellites, Effectivity Satellites, Point-in-Time tables, Bridge tables, Business Vault patterns, hash key generation, DDL for all entity types, insert-only DML load patterns, pipeline architecture, and constructing Information Marts from the vault.

  skills/data_vault_2/SKILL.md on GitHub (opens in a new tab)

• datahub-catalog

  DataHub data catalog — metadata ingestion recipes (PostgreSQL/Hive/Spark/dbt/Airflow/Kafka/S3), Python SDK (DatahubRestEmitter, MetadataChangeProposalWrapper), column-level lineage (FineGrainedLineage), GMS/MCE/MCP architecture, Kubernetes Helm deployment, CLI operations, search/discovery REST API and GraphQL

  skills/datahub_catalog/SKILL.md on GitHub (opens in a new tab)

• dbt-core

  dbt Core (adapter-agnostic) — project structure, models (SQL/Python), sources and refs, materializations (table/view/incremental/ephemeral), incremental strategies (append/merge/delete+insert), is_incremental() macro, snapshots (SCD Type 2), seeds, tests (generic/singular/severity), Jinja macros, dbt-utils package, node selection syntax (graph operators), hooks, exposures, metrics (MetricFlow), C…

  skills/dbt_core/SKILL.md on GitHub (opens in a new tab)

• dbt-macros

  dbt Jinja macros — macro authoring fundamentals, Jinja syntax (blocks/filters/tests), context variables (this/target/adapter/execute), run_query, adapter.dispatch for cross-database macros, generate_schema_name, hooks, custom materializations, dbt-utils patterns

  skills/dbt_macros/SKILL.md on GitHub (opens in a new tab)

• dbt_trino

  Use when writing, configuring, or optimizing dbt projects targeting Trino or Starburst — covering profiles.yml setup, all authentication methods, materializations (table/view/incremental/materialized_view/ephemeral), incremental strategies (append/merge/delete+insert), table properties (format/partitioning/sorted_by), on_schema_change, seeds, snapshots, grants, session properties, data modeling p…

  skills/dbt_trino/SKILL.md on GitHub (opens in a new tab)

• de-architecture-decision

  DE architecture decision records (ADR) — ADR template, trade-off analysis framework, technology evaluation criteria, common DE architecture decisions (storage format, orchestrator, streaming vs batch, lakehouse vs warehouse), decision reversibility, stakeholder communication

  skills/de_adr/SKILL.md on GitHub (opens in a new tab)

• de-cost-optimization

  DE cost optimization — query cost analysis (Trino/Spark/ClickHouse/BigQuery), compute right-sizing, storage tier recommendations, spot/preemptible instances, partition pruning, Z-order/clustering, materialized view economics, data lifecycle policies, FinOps tagging, cost attribution, budget alerts

  skills/de_cost_optimization/SKILL.md on GitHub (opens in a new tab)

• de-postmortem-writer

  DE blameless postmortem — incident timeline reconstruction, impact quantification, root cause chain (5 Whys), corrective actions (CAPA), postmortem document template, severity classification, SLA breach reporting, communication templates, lessons-learned facilitation, postmortem review checklist

  skills/de_postmortem/SKILL.md on GitHub (opens in a new tab)

• de-production-readiness

  Data engineering production readiness — idempotency patterns (INSERT ON CONFLICT, MERGE, partition overwrite), retry strategies (exponential backoff, Airflow retry config), SLA monitoring (Airflow sla_miss_callback, Prometheus PromQL), data freshness checks (Soda freshness, max event_time), structured logging (JSON logs, OpenTelemetry tracing), data reconciliation (row count, checksum, duplicate…

  skills/de_production_readiness/SKILL.md on GitHub (opens in a new tab)

• de-root-cause-analysis

  DE pipeline root cause analysis — failure taxonomy, lineage tracing, upstream/downstream impact, log analysis patterns, Airflow/Spark/dbt failure diagnosis, data quality anomaly RCA, 5-Whys template, incident timeline reconstruction, runbook-style investigation checklists

  skills/de_rca/SKILL.md on GitHub (opens in a new tab)

• delta-lake

  Delta Lake — table DDL (CREATE/PARTITIONED BY/LOCATION/TBLPROPERTIES), DML (INSERT/UPDATE/DELETE/MERGE), MERGE patterns (upsert/SCD2/CDC), schema evolution (mergeSchema/overwriteSchema/ALTER TABLE), OPTIMIZE compaction, Z-ORDER BY data skipping, VACUUM, Time Travel (VERSION AS OF/TIMESTAMP AS OF), RESTORE, DESCRIBE HISTORY, shallow/deep clone, Change Data Feed, streaming read/write, deletion vect…

  skills/delta_lake/SKILL.md on GitHub (opens in a new tab)

• docker-data-environments

  Docker for data engineering environments — multi-stage Dockerfiles for dbt/Spark/Airflow images, BuildKit layer caching (--mount=type=cache), private registries (ghcr.io/Harbor), docker buildx multi-platform builds, Docker Compose local data stacks (Spark+Airflow+Kafka+MinIO+Postgres), BuildKit secrets for private PyPI, security hardening (non-root user, slim base images, read-only FS), CI/CD wit…

  skills/docker_data_envs/SKILL.md on GitHub (opens in a new tab)

• duckdb

  DuckDB in-process OLAP analytics — reading Parquet/CSV/JSON/Iceberg/Delta from S3/local, SQL (window functions, PIVOT, ASOF join, QUALIFY), Python API (duckdb.connect, fetchdf, register, UDFs), extensions (httpfs/iceberg/delta/postgres), performance tuning, COPY TO, persistent DB

  skills/duckdb/SKILL.md on GitHub (opens in a new tab)

• feature-store

  Feast feature store — feature repo layout, Entity/FeatureView/FeatureService definitions, offline store (BigQuery/Redshift/Snowflake/file), online store (Redis/DynamoDB/SQLite), point-in-time correct joins, feast materialize, feast apply, Python SDK get_online_features/get_historical_features, push sources, streaming feature views, Airflow integration

  skills/feature_store/SKILL.md on GitHub (opens in a new tab)

• github-actions-dataops

  Use when building or reviewing GitHub Actions CI/CD pipelines for data engineering — covering dbt slim CI (state:modified+, --defer, manifest.json), SQLFluff SQL linting with PR annotations, Airflow DAG integrity tests (pytest/DagBag), Great Expectations and Soda data quality gates, multi-stage Docker image builds for Spark/dbt/Airflow pushed to ghcr.io, secrets management with OIDC for AWS/GCP (…

  skills/github_actions_dataops/SKILL.md on GitHub (opens in a new tab)

• great-expectations

  Great Expectations (GX) — DataContext setup, Data Sources (Pandas/Spark/SQL/file), Expectation Suites, built-in expectations (null/uniqueness/range/set/regex/table-level/statistical), Validation Definitions, Checkpoints, Data Docs, Airflow integration, custom expectations, dbt integration, CI/CD usage, severity levels (warning/critical)

  skills/great_expectations/SKILL.md on GitHub (opens in a new tab)

• kimball_data_modeling

  Use when designing or implementing dimensional data models following Kimball methodology — fact tables (transaction/snapshot/accumulating), dimension tables, SCD types (1/2/3/4/6), star/snowflake schemas, surrogate keys, conformed dimensions, role-playing dimensions, junk dimensions, bridge tables, date dimensions, degenerate dimensions, DDL patterns, and DML update strategies for loading and mai…

  skills/kimball_data_modeling/SKILL.md on GitHub (opens in a new tab)

• kubernetes-data-platform

  Data platform on Kubernetes — Spark-on-K8s (spark-submit, pod templates, dynamic allocation, RBAC), Airflow on K8s (Helm chart, KubernetesExecutor, KubernetesPodOperator, git-sync, DAG storage), resource management, namespaces, autoscaling

  skills/kubernetes_data/SKILL.md on GitHub (opens in a new tab)

• mage-ai-pipelines

  Mage AI data pipelines — block types (data loader/transformer/exporter/sensor/custom), pipeline YAML, hybrid SQL+Python blocks, triggers (schedule/event/API), streaming pipelines, dbt integration, Spark integration, Docker/Kubernetes deployment, io_config.yaml, pipeline variables, callbacks, backfills

  skills/mage_ai/SKILL.md on GitHub (opens in a new tab)

• mcp-server

  MCP (Model Context Protocol) server development — FastMCP/Python SDK, tools/resources/prompts primitives, STDIO and Streamable HTTP transports, Claude Desktop/Claude Code client config, MCP Inspector testing, security best practices (input validation/OAuth2/confused deputy prevention), production Docker/Kubernetes deployment, agentic data platform integration

  skills/mcp_server/SKILL.md on GitHub (opens in a new tab)

• medallion_architecture

  Use when designing or implementing Medallion (Bronze/Silver/Gold) data lakehouse architecture — layer definitions, DDL for each layer, DML load and update patterns, incremental pipeline design, deduplication strategies (row_number/merge/hash/CDC/watermark), schema evolution, data quality gates, partitioning per layer, late-arriving data, orchestration patterns, and best practices for Iceberg-base…

  skills/medallion_architecture/SKILL.md on GitHub (opens in a new tab)

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