| (1) Task mentions "fix", "error", "broken", "failing", "debug", "wrong", or "not working" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.
npx skills add https://github.com/AltimateAI/data-engineering-skills --skill debugging-dbt-errors
Read the full error. Check upstream first. ALWAYS run dbt build after fixing.
dbt build after fixing - compile is NOT enough to verify the fixdbt compile --select <model_name>
# or
dbt build --select <model_name>
Read the COMPLETE error message. Note the file, line number, and specific error.
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.
cat target/compiled/<project>/<path>/<model_name>.sql
See the actual SQL that will run.
| 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 |
# 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.
Common fixes:
| Error | Fix |
|-------|-----|
| Column not found | Check upstream model's output columns |
| Ambiguous column | Add table alias: table.column |
| Type mismatch | Add explicit CAST() |
| Division by zero | Use NULLIF(divisor, 0) |
| Jinja error | Check matching {{ }} and {% %} |
dbt build --select <model_name>
3-Failure Rule: If build fails 3+ times, STOP. Step back and:
# Preview the data
dbt show --select <model_name> --limit 10
# Run tests
dbt test --select <model_name>
After fixing, re-read the original request and verify:
# Find downstream models
grep -r "ref('<model_name>')" models/ --include="*.sql"
# Rebuild downstream
dbt build --select <model_name>+
{{ }} and {% %}target/compiled/LLM-driven hypothesis generation/testing on tabular data. Three methods: HypoGeniC (data-driven), HypoRefine (literature+data), Union. Iterative refinement, Redis caching, multi-hypothesis inference. Manual: hypothesis-generation; ideation: scientific-brainstorming.
This skill should be used when the user asks to \"automate SQL injection testing,\" \"enumerate database structure,\" \"extract database credentials using sqlmap,\" \"dump tables and columns...
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
Bisect a ClickHouse regression using pre-built master binaries from CI. Use when the user wants to find the commit that introduced a bug.
QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.
End-to-end smoke test for the public Errors HTTP API (error groups). Seeds failed runs into ClickHouse so the error materialized views populate, then drives the real endpoints against the running webapp — list (with filters + pagination), retrieve, resolve/ignore/unresolve, the `filter[error]` runs filter, user attribution via the `trigger.dev mint-token` -> JWT exchange, and the 401/403/404 negatives. Use for "smoke test the errors API", "test the errors API e2e", "prove the errors endpoints work", or to re-verify after changes.
Strix SQL 注入测试手册,覆盖 union、blind、error-based 与 ORM 绕过技巧;触发名:strix-sql-injection
> Pull and interpret production experiment query-performance data from the staff-only slowest experiment queries, precompute read/build health, and preaggregation cache footprint. and response field semantics (exception codes, exposure paths, precompute skip reasons, job states). Use when investigating slow or failing experiment queries, precompute regressions, 307/159/241 errors, preaggregation table growth, or when asked how experiment query performance or the precompute rollout is doing in production.
Take altimateai/debugging-dbt-errors from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.