> 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.
curl "https://skillshub.wtf/dbt-labs/dbt-agent-skills/running-dbt-commands?format=md"Running dbt Commands
Preferences
- Use MCP tools if available (
dbt_build,dbt_run,dbt_show, etc.) - they handle paths, timeouts, and formatting automatically - Use
buildinstead ofrunortest-testdoesn't refresh the model, so testing a model change requiresbuild.builddoes arunand atestof each node (model, seed, snapshot) in the order of the DAG - Always use
--quietwith--warn-error-options '{"error": ["NoNodesForSelectionCriteria"]}'to reduce output while catching selector typos - Always use
--select- never run the entire project without explicit user approval
Quick Reference
# 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.
| Flavor | Location | Notes |
|---|---|---|
| dbt Core | Python venv | pip show dbt-core or uv pip show dbt-core |
| dbt Fusion | ~/.local/bin/dbt or dbtf | Faster and has stronger SQL comprehension |
| dbt Cloud CLI | ~/.local/bin/dbt | Go-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:
| Operator | Meaning | Example |
|---|---|---|
model+ | Model and all downstream | stg_orders+ |
+model | Model and all upstream | +dim_customers |
+model+ | Both directions | +orders+ |
model+N | Model and N levels downstream | stg_orders+1 |
--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:
--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
List
Use dbt list to preview what will be selected before running. Helpful for validating complex selectors.
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.
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).
--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:
# 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, warnexecution_time: seconds spent executingcompiled_code: rendered SQLadapter_response: database metadata (rows affected, bytes processed)
Defer (Skip Upstream Builds)
Reference production data instead of building upstream models:
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
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:
dbt run --static-analysis=off
dbt run --static-analysis=unsafe
Common Mistakes
| Mistake | Fix |
|---|---|
Using test after model change | Use build - test doesn't refresh the model |
Running without --select | Always specify what to run |
Using --quiet without warn-error | Add --warn-error-options '{"error": ["NoNodesForSelectionCriteria"]}' |
Running dbt expecting Fusion when we are in a venv | Use dbtf or ~/.local/bin/dbt |
Adding LIMIT to SQL in dbt_show | Use limit parameter instead |
| Vars with special characters | Pass as simple string, no \ or \n |
> related_skills --same-repo
> using-dbt-for-analytics-engineering
Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.
> troubleshooting-dbt-job-errors
Diagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues. Use when a dbt Cloud/platform job fails and you need to diagnose the root cause, especially when error messages are unclear or when intermittent failures occur. Do not use for local dbt development errors.
> fetching-dbt-docs
Retrieves and searches dbt documentation pages in LLM-friendly markdown format. Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer.
> configuring-dbt-mcp-server
Generates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt. Use when setting up, configuring, or troubleshooting the dbt MCP server for AI tools like Claude Desktop, Claude Code, Cursor, or VS Code.