Running Dbt Commands logo

Running Dbt Commands

Organization
dbt-labs
running-dbt-commands

Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles, or show queries via dbt CLI. Use when unsure which dbt executable to use or how to format command parameters.

Overview

Publisherdbt-labs
Repositorydbt-agent-skills
Skill namerunning-dbt-commands
Stars
732
Forks
63
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dbt-labs on GitHub. Read the source before you install it.

Installation

Install the Running Dbt Commands AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dbt-labs/dbt-agent-skills/tree/main/skills/dbt/skills/running-dbt-commands
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dbt-labs/dbt-agent-skills.git /tmp/dbt-agent-skills
mkdir -p .claude/skills
cp -r /tmp/dbt-agent-skills/skills/dbt/skills/running-dbt-commands .claude/skills/running-dbt-commands
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Running Dbt Commands in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Running Dbt Commands on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Running Dbt Commands is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Running dbt Commands

Preferences

  1. Use MCP tools if available (dbt_build, dbt_run, dbt_show, etc.) - they handle paths, timeouts, and formatting automatically
  2. Always use build — even when users say "run" - When a user asks to "run" a model, recommend dbt build instead. build = run + test in one step, so it catches data quality issues immediately. dbt run alone is almost never the right answer during development.
  3. Always use --quiet with --warn-error-options '{"error": ["NoNodesForSelectionCriteria"]}' to reduce output while catching selector typos
  4. Always use --select - never run the entire project without explicit user approval

Quick Reference

bash
# Standard command pattern
dbt build --select my_model --quiet --warn-error-options '{"error": ["NoNodesForSelectionCriteria"]}'

# Preview model output
dbt show --select my_model --limit 10

# Run inline SQL query
dbt show --inline "select * from {{ ref('orders') }}" --limit 5

# With variables (JSON format for multiple)
dbt build --select my_model --vars '{"key": "value"}'

# Full refresh for incremental models
dbt build --select my_model --full-refresh

# List resources before running
dbt list --select my_model+ --resource-type model

dbt CLI Flavors

Three CLIs exist. Ask the user which one if unsure.

FlavorLocationNotes
dbt CorePython venvpip show dbt-core or uv pip show dbt-core
dbt Fusion~/.local/bin/dbt or dbtfFaster and has stronger SQL comprehension
dbt Cloud CLI~/.local/bin/dbtGo-based, runs on platform

Common setup: Core in venv + Fusion at ~/.local/bin. Running dbt uses Core. Use dbtf or ~/.local/bin/dbt for Fusion.

Selectors

Always provide a selector. Graph operators:

OperatorMeaningExample
model+Model and all downstreamstg_orders+
+modelModel and all upstream+dim_customers
+model+Both directions+orders+
model+NModel and N levels downstreamstg_orders+1
bash
--select my_model              # Single model
--select staging.*             # Path pattern
--select fqn:*stg_*            # FQN pattern
--select model_a model_b       # Union (space)
--select tag:x,config.mat:y    # Intersection (comma)
--exclude my_model             # Exclude from selection

Resource type filter:

bash
--resource-type model
--resource-type test --resource-type unit_test

Valid types: model, test, unit_test, snapshot, seed, source, exposure, metric, semantic_model, saved_query, analysis

Fusion: --resource-type is not supported with dbt test (dbt-fusion#1628). To run unit tests in Fusion:

  • dbt build --select model_name — builds the model first, then runs all tests including unit tests
  • dbt build --select unit_test_name — targets a specific unit test by name
  • dbt list --resource-type unit_test — lists unit test names for use in selectors

List

Use dbt list to preview what will be selected before running. Helpful for validating complex selectors.

bash
dbt list --select my_model+              # Preview selection
dbt list --select my_model+ --resource-type model  # Only models
dbt list --output json                   # JSON output
dbt list --select my_model --output json --output-keys unique_id name resource_type config

Available output keys for --output json: unique_id, name, resource_type, package_name, original_file_path, path, alias, description, columns, meta, tags, config, depends_on, patch_path, schema, database, relation_name, raw_code, compiled_code, language, docs, group, access, version, fqn, refs, sources, metrics

Show

Preview data with dbt show. Use --inline for arbitrary SQL queries.

bash
dbt show --select my_model --limit 10
dbt show --inline "select * from {{ ref('orders') }} where status = 'pending'" --limit 5

Important: Use --limit flag, not SQL LIMIT clause.

Variables

Pass as STRING, not dict. No special characters (\, \n).

bash
--vars 'my_var: value'                              # Single
--vars '{"k1": "v1", "k2": 42, "k3": true}'         # Multiple (JSON)

Analyzing Run Results

After a dbt command, check target/run_results.json for detailed execution info:

bash
# Quick status check
cat target/run_results.json | jq '.results[] | {node: .unique_id, status: .status, time: .execution_time}'

# Find failures
cat target/run_results.json | jq '.results[] | select(.status != "success")'

Key fields:

  • status: success, error, fail, skipped, warn
  • execution_time: seconds spent executing
  • compiled_code: rendered SQL
  • adapter_response: database metadata (rows affected, bytes processed)

Defer (Skip Upstream Builds)

Reference production data instead of building upstream models:

bash
dbt build --select my_model --defer --state prod-artifacts

Flags:

  • --defer - enable deferral to state manifest
  • --state <path> - path to manifest from previous run (e.g., production artifacts)
  • --favor-state - prefer node definitions from state even if they exist locally
bash
dbt build --select my_model --defer --state prod-artifacts --favor-state

Static Analysis (Fusion Only)

Override SQL analysis for models with dynamic SQL or unrecognized UDFs:

bash
dbt run --static-analysis=off
dbt run --static-analysis=unsafe

Common Mistakes

MistakeFix
Using test after model changeUse build - test doesn't refresh the model
Running without --selectAlways specify what to run
Using --quiet without warn-errorAdd --warn-error-options '{"error": ["NoNodesForSelectionCriteria"]}'
Running dbt expecting Fusion when we are in a venvUse dbtf or ~/.local/bin/dbt
Schema errors after changing files in FusionRun dbt clean to clear the stale schema cache, then re-run
Adding LIMIT to SQL in dbt_showUse limit parameter instead
Vars with special charactersPass as simple string, no \ or \n

Frequently asked questions

What does the Running Dbt Commands AI skill do?

Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles, or show queries via dbt CLI. Use when unsure which dbt executable to use or how to format command parameters.

Why use Running Dbt Commands on TypingMind?

Because you install it once and use it with any model. Running Dbt Commands is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Running Dbt Commands in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dbt-labs/dbt-agent-skills/tree/main/skills/dbt/skills/running-dbt-commands. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Running Dbt Commands?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Running Dbt Commands?

As many as you like. As long as a model supports skills, you can use Running Dbt Commands with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Running Dbt Commands AI skill free?

Yes. It is published on GitHub by dbt-labs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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