Claude-skill-registry debugging-dbt-errors
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/debugging-dbt-errors" ~/.claude/skills/majiayu000-claude-skill-registry-debugging-dbt-errors && rm -rf "$T"
manifest:
skills/data/debugging-dbt-errors/SKILL.mdsource content
dbt Troubleshooting
Read the full error. Check upstream first. ALWAYS run
after fixing.dbt build
Critical Rules
- ALWAYS run
after fixing - compile is NOT enough to verify the fixdbt build - If fix fails 3+ times, stop and reassess your entire approach
- Verify data after build - build passing doesn't mean output is correct
Workflow
1. Get the Full Error
dbt compile --select <model_name> # or dbt build --select <model_name>
Read the COMPLETE error message. Note the file, line number, and specific error.
2. Inspect Actual Data (For Data Issues)
Before fixing "wrong output" or "incorrect results", query the actual data:
# Preview current output dbt show --select <model_name> --limit 20 # Check specific values with inline query dbt show --inline "select * from {{ ref('model_name') }} where <condition>" --limit 10 # Compare with expected - look for patterns dbt show --inline "select column, count(*) from {{ ref('model_name') }} group by 1 order by 2 desc" --limit 10
Understand what's wrong before attempting to fix it.
3. Read Compiled SQL
cat target/compiled/<project>/<path>/<model_name>.sql
See the actual SQL that will run.
4. Analyze Error Type
| Error Type | Look For |
|---|---|
| Compilation Error | Jinja syntax, missing refs, YAML issues |
| Database Error | Column not found, type mismatch, SQL syntax |
| Dependency Error | Missing model, circular reference |
5. Check Upstream Models
# Find what this model references grep -E "ref\(|source\(" models/<path>/<model_name>.sql # Read upstream model to verify columns cat models/<path>/<upstream_model>.sql
Many errors come from upstream changes, not the current model.
6. Apply Fix
Common fixes:
| Error | Fix |
|---|---|
| Column not found | Check upstream model's output columns |
| Ambiguous column | Add table alias: |
| Type mismatch | Add explicit |
| Division by zero | Use |
| Jinja error | Check matching and |
7. Rebuild (MANDATORY)
dbt build --select <model_name>
3-Failure Rule: If build fails 3+ times, STOP. Step back and:
- Re-read the original error
- Check if your entire approach is wrong
- Consider alternative solutions
8. Verify Fix
# Preview the data dbt show --select <model_name> --limit 10 # Run tests dbt test --select <model_name>
9. Re-review Logic Against Requirements
After fixing, re-read the original request and verify:
- Does the output match what the user asked for?
- Are the column names exactly as requested?
- Is the calculation logic correct per the requirements?
- Did you solve the actual problem, not just make the error go away?
10. Check Downstream Impact
# Find downstream models grep -r "ref('<model_name>')" models/ --include="*.sql" # Rebuild downstream dbt build --select <model_name>+
Error Categories
Compilation Errors
- Check Jinja syntax: matching
and{{ }}{% %} - Verify macro arguments
- Check YAML indentation
Database Errors
- Read compiled SQL in
target/compiled/ - Check column names against upstream
- Verify data types
Test Failures
- Read the test SQL to understand what it checks
- Compare your model output to expected behavior
- Check column names, data types, NULL handling
Anti-Patterns
- Making random changes without understanding the error
- Assuming the current model is wrong before checking upstream
- Not reading the FULL error message
- Declaring "fixed" without running build
- Getting stuck making small tweaks instead of reassessing