skills/dbt/SKILL.md
Data transformation with dbt (data build tool) — SQL-based modeling, testing, documentation, incremental builds, Jinja macros, snapshots, semantic layer, and deployment. MANDATORY TRIGGERS: dbt, data build tool, dbt-core, dbt Cloud, dbt run, dbt build, dbt test. Also trigger when the user wants to build SQL transformation pipelines, define data models with refs, write data quality tests, create incremental models, use Jinja macros in SQL, manage data warehouse transformations, or set up analytics engineering workflows. When in doubt about whether to use this skill for data transformation tasks, use it.
npx skillsauth add abhisheksharma-17/skills-graph dbtInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Version tracked: dbt-core 1.11.11 (stable) / 2.0.0a1 (alpha) · Source: https://docs.getdbt.com
| File | Read When |
|------|-----------|
| references/00-overview.md | Starting with dbt, understanding core concepts, installation |
| references/01-project-structure.md | Setting up dbt_project.yml, profiles.yml, directory layout, naming conventions |
| references/02-models-materializations.md | Writing SQL/Python models, choosing materializations (table, view, incremental, ephemeral) |
| references/03-sources-refs.md | Declaring sources, using ref() and source(), source freshness, lineage |
| references/04-incremental-models.md | Building incremental models, strategies (merge, append, delete+insert), is_incremental() |
| references/05-tests.md | Writing data tests (unique, not_null, relationships), singular tests, custom generic tests |
| references/06-jinja-macros.md | Jinja templating in SQL, writing macros, built-in functions, whitespace control |
| references/07-seeds-snapshots.md | Loading CSV seeds, SCD Type 2 snapshots, timestamp/check strategies |
| references/08-packages.md | Installing packages (dbt-utils, dbt-expectations), Hub/Git/private packages |
| references/09-hooks-operations.md | Pre/post hooks, on-run-start/end, run-operation, grants, custom SQL execution |
| references/10-semantic-layer.md | Defining metrics, semantic models, entities, measures, dimensions, MetricFlow |
| references/11-governance-mesh.md | Model access (public/protected/private), contracts, versions, groups, dbt Mesh |
| references/12-cli-deployment.md | CLI commands (run, build, test), node selection, CI/CD, state-aware builds |
# dbt Core (Python)
pip install dbt-core dbt-postgres # or dbt-snowflake, dbt-bigquery, dbt-databricks
# Initialize a new project
dbt init my_project
# Install dependencies
dbt deps
development
High-throughput LLM inference and serving engine with PagedAttention, continuous batching, and OpenAI-compatible API. MANDATORY TRIGGERS: vLLM, vllm, LLM serving, LLM inference engine, PagedAttention. Also trigger when the user wants to serve LLMs in production, deploy models with tensor parallelism, use speculative decoding, quantize models for inference, build OpenAI-compatible API servers, or optimize LLM throughput and latency. When in doubt about whether to use this skill for LLM serving tasks, use it.
tools
Type-safe Python agent framework for building production-grade GenAI applications with Pydantic validation, structured outputs, and dependency injection. MANDATORY TRIGGERS: pydantic-ai, pydantic_ai, PydanticAI, pydantic ai agent. Also trigger when the user wants to build type-safe AI agents in Python, create structured LLM outputs with Pydantic models, implement dependency injection for agents, use tools/capabilities with LLMs, or build multi-agent systems with Python type safety. When in doubt about whether to use this skill for Python AI agent tasks, use it.
development
Durable execution platform for building fault-tolerant workflows, long-running processes, and resilient distributed applications. MANDATORY TRIGGERS: temporal, temporal.io, temporalio, durable execution, workflow orchestration engine. Also trigger when the user wants to build fault-tolerant workflows, implement saga patterns, create long-running distributed processes, orchestrate microservices with retries and timeouts, or build durable AI agent pipelines. When in doubt about whether to use this skill for workflow orchestration or durable execution tasks, use it.
tools
AI framework for building RAG pipelines, agents, workflows, and data-augmented LLM applications with 300+ integrations. MANDATORY TRIGGERS: llamaindex, llama-index, llama_index, LlamaIndex, VectorStoreIndex, SimpleDirectoryReader, LlamaHub, LlamaParse. Also trigger when the user wants to build RAG applications with LlamaIndex, create document indexing pipelines, build agentic workflows with tool calling, implement structured data extraction from documents, or connect LLMs to custom data sources. When in doubt about whether to use this skill for RAG, document indexing, or LLM data augmentation tasks, use it.