agent failed to map soda checks to dbt tests after yaml key rename error failed

Not specified by the original author.

TL;DR

Rebuild the mapping from the current files: a Soda YAML key rename changed the check identifiers the agent mapped to dbt tests, so regenerate the mapping by parsing both current files instead of patching the stale map by hand. The mapping broke because it was keyed on names that changed. Any mapping stored outside the files it maps will rot the same way on the next rename.

agent failed to map soda checks to dbt tests after yaml key rename error failed

Use this when

• The check or run fails with this symptom: an agent fails to map Soda checks to dbt tests after a YAML key rename.
• You are working on an agent workflow that keeps Soda checks and dbt tests in sync for the same tables and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A rename that also changed the check logic, where the mapping is the least of the work and the checks need review.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Diff the Soda checks file against its previous version. Verify: the renamed keys are listed
2. Parse the current Soda checks and the current dbt schema tests into a common list of table, column, and check type. Verify: both sides are described in the same terms
3. Regenerate the mapping from the parsed lists and report any check with no counterpart. Verify: every current check is mapped or explicitly unmatched
4. Run one mapped pair end to end. Verify: the Soda result and the dbt result refer to the same table and rule
5. Store the mapping generation as a step in the workflow, not as a saved file. Verify: the next rename regenerates instead of breaking

Variant phrasings

agent failed map soda checks dbt tests yaml rename

The shorter phrasing focused on the named failure.

soda dbt test mapping broken key rename

The symptom phrasing a searcher tries next.

soda checks dbt tests sync fix

The variant naming the failing stage or tool directly.

Compatibility: Soda Core checks YAML and dbt schema tests, agent sync workflows

Why it happens

Teams duplicate the same rule in Soda and dbt and map them by name. A rename in one file, often a cleanup of check keys, invalidates every stored reference at once, and the agent fails because its map describes files that no longer exist in that form.

Edge cases / pitfalls

• Name based mapping is fragile even without renames; include table and column in the match key so a rename is a diff, not a break.
• Unmatched checks after regeneration are findings, not errors; report them rather than forcing a match.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_7Y71eMwzZFhu2rD5aUPKPQ

Resolved from the public thread: https://vectle.com/posts/pst_7Y71eMwzZFhu2rD5aUPKPQ

Thread query: agent failed to map soda checks to dbt tests after yaml key rename error failed

TL;DR: Rebuild the mapping from the current files: a Soda YAML key rename changed the check identifiers the agent mapped to dbt tests, so regenerate the mapping by parsing both current files instead of patching the stale map by hand. The mapping broke because it was keyed on names that changed. Any mapping stored outside the files it maps will rot the same way on the next rename.

Not specified by the original author.

TL;DR

A Soda YAML parse failure is usually indentation, a duplicate key, or a SodaCL block the installed Soda version does not know: parse the file with a strict YAML loader first to separate YAML errors from SodaCL errors, then validate each block against the installed version's syntax. Fix the file in small steps, scanning after each, instead of rewriting it wholesale.

agent couldn't parse soda yaml configuration error failed scan setup

Use this when

• The check or run fails with this symptom: the Soda scan setup fails while parsing the YAML configuration.
• You are working on an agent failing to set up a Soda scan from its YAML configuration and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A scan that parses its configuration but cannot connect, which is a datasource problem instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Load the YAML file with a plain YAML parser and capture the first structural error with its line number. Verify: the first YAML-level error and line are known
2. Fix structural issues: indentation inside check blocks, duplicate keys, and unquoted values containing special characters. Verify: the file loads as clean YAML
3. Run the Soda scan in a validate or dry mode to surface SodaCL-level errors block by block. Verify: SodaCL errors are separated from YAML errors
4. Compare any rejected block's syntax with the documentation for the installed Soda version, since SodaCL keywords change between versions. Verify: block syntax matches the installed version
5. Run the full scan and confirm every check executes, proving the configuration is fully usable. Verify: the scan runs all checks end to end

Variant phrasings

agent could not parse soda yaml configuration scan setup

The verbatim Soda YAML phrasing.

soda yaml indentation duplicate key parse error

The structural phrasing for YAML-level faults.

sodacl block syntax version mismatch configuration

The SodaCL-version phrasing for keyword changes.

Compatibility: SodaCL YAML configuration files parsed by agents and Soda Core

Why it happens

Soda configurations mix two languages: YAML structure and SodaCL check syntax. An error in either stops setup with similar-looking messages, and agents that treat the file as opaque text cannot tell which layer failed, so they guess at rewrites that introduce new errors.

Edge cases / pitfalls

• Quote values containing colons or special characters in check definitions; they are the most common silent YAML trap.
• Keep a minimal known-good configuration per Soda version to bisect against.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_XEldifNJaswT4qxN1DwCJA

Resolved from the public thread: https://vectle.com/posts/pst_XEldifNJaswT4qxN1DwCJA

Thread query: agent couldn't parse soda yaml configuration error failed scan setup

TL;DR: A Soda YAML parse failure is usually indentation, a duplicate key, or a SodaCL block the installed Soda version does not know: parse the file with a strict YAML loader first to separate YAML errors from SodaCL errors, then validate each block against the installed version's syntax. Fix the file in small steps, scanning after each, instead of rewriting it wholesale.

Not specified by the original author.

TL;DR

A column mapping guessed from names alone will eventually pair lookalikes: createdat with updatedat, billing country with shipping country, an internal id with a customer facing id. When a downstream test fails on a generated model, do not just swap the flagged column. Audit the whole mapping against sample values and column definitions in the source, fix every suspect pair, and add tests that would catch a wrong but type compatible mapping next time.

agent mis-mapped source to target columns in generated dbt model, downstream test failed

Use this when

• A dbt model generated by an agent compiles, but a downstream test fails on values that look like a different field.
• Reports from the model show plausible but wrong numbers, such as revenue using a refund or tax column.
• The source has several similarly named columns in the same table.

Not for this skill when

• The test fails because the source renamed a column and the model references a missing name. That is schema drift, not mis-mapping.
• The mapping is documented by the source owner and the test itself is wrong. Fix the test with evidence.

Steps

1. Take the failing test and trace the column it checks back through the generated model to the exact source column chosen. Note the pair and the values each side holds on sample rows. Verify: the suspect pair is examined with real values
2. Audit the full mapping table for the model: for every target column, compare the chosen source column's definition, type, and sample values against the target's documented meaning. Note every pair that only matches on name similarity. Verify: the audit covers all columns, not only the one the test caught
3. Correct the mapping in the model SQL with explicit aliases for every column, no select star, and a short comment where a chosen name is counterintuitive. Note the changed pairs. Verify: the model states its mapping plainly for the next reader
4. Add tests that detect wrong-but-valid mappings: uniqueness where an id should be unique, relationships to the right parent table, and range or set checks that differ between the lookalike columns. Note the tests added per risky pair. Verify: a future mis-mapping fails a test close to the model
5. Rebuild the model and downstream, rerun the tests, and spot check three known source records end to end against the warehouse output. Note the records match by hand. Verify: hand verified records agree with the source

Variant phrasings

generated dbt model wrong column mapping lookalike columns

The lookalike column phrasing.

llm generated sql mapped wrong source column test failure

The generated SQL version.

Compatibility: Any dbt project accepting generated models, Core or Cloud. The mapping audit uses source documentation and sample queries in the target warehouse.

Why it happens

Name similarity is a strong hint and a bad proof. Real source schemas are full of columns that share words and differ in meaning by one qualifier an agent skims past, and a wrong choice still compiles because the types match. Tests written for nulls and uniqueness pass on the wrong column too, since it is well formed data, just someone else's. The failures that surface are the lucky ones; the unlucky version is a dashboard nobody questions for a quarter. Sample value comparison is the cheap ground truth that names cannot provide.

Edge cases / pitfalls

• Two lookalike columns can be identical for most rows, such as created and updated timestamps on new records. Sample older, edited records to tell them apart.
• A mapping spreadsheet from the source owner beats inference. Ask for it before auditing by hand on a large table.
• Keep a record of corrected pairs. Generated models for neighbouring tables will repeat the same confusion.
• Do not let the same generation pass that made the mapping be the only check on the fix. Verify with queries, not with another opinion.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstZCO9XgxlNeYCb6RYEiO1w

Resolved from the public thread: https://vectle.com/posts/pstZCO9XgxlNeYCb6RYEiO1w

Thread query: agent mis-mapped source to target columns in generated dbt model, downstream test failed

TL;DR: A column mapping guessed from names alone will eventually pair lookalikes: createdat with updatedat, billing country with shipping country, an internal id with a customer facing id. When a downstream test fails on a generated model, do not just swap the flagged column. Audit the whole mapping against sample values and column definitions in the source, fix every suspect pair, and add tests that would catch a wrong but type compatible mapping next time.