agent+could+not+parse+Looker+dashboard+YAML+export+quoted+filter

Not specified by the original author.

TL;DR

Fix this in Looker by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

agent couldn't parse Looker dashboard export error failed parsing failure

Use this when

• The operation in Looker fails with this exact message: agent couldn't parse Looker dashboard export error failed parsing failure.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Looker, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Verify: parse the full file and confirm row counts match the source export.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Looker on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in Looker so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Parsers fail on the first malformed record; a truncated array, extra field or bad byte stops the whole import unless the reader isolates bad rows.

Variant phrasings

agent couldn't parse Looker dashboard export error failed parsing fail

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: agent couldn't parse Looker dashboard export error failed parsing fail

The how-to phrasing for the same failure in Looker. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Looker troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Looker at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstxWjOqMyS5XQBF9dZEnesw

Resolved from the public thread: https://vectle.com/posts/pstxWjOqMyS5XQBF9dZEnesw

Thread query: agent couldn't parse Looker dashboard export error failed parsing failure

TL;DR: Fix this in Looker by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Repair the input file, then make the parser prove it before the build starts. Load the audience export with a strict parser, list the invalid filters it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

parser failed on audience export with invalid filters: agent could not parse the file and the run failed with error

Use this when

• An agent run stops at intake or early in the build with a parse error naming the audience export.
• Inspecting the file shows invalid filters that a human can spot once they are listed.
• The same class of malformed file has broken more than one run.

Not for this skill when

• The file parses and the Superset REST API rejects the resulting dashboard. That is an API validation problem, not a parse failure.
• The file is generated by the agent itself in the same run. Fix the generator step rather than the parser.

Steps

1. Reproduce the failure locally by loading the exact audience export from the failed run with the same parser the agent uses. Verify: the local load fails with the same error the run recorded
2. Run a structural check over the file that lists the invalid filters explicitly, with locations, instead of stopping at the first defect. Verify: the check outputs a complete defect list for the file
3. Repair the file at the source, correcting each listed defect, or return the defect list to the file owner if the right values are a business decision. Verify: the repaired file passes the structural check with no defects
4. Add a preflight validation step to the agent that runs the check before any API call and fails the run with the defect list attached. Verify: a deliberately broken test file fails in preflight with a readable report
5. Define the expected file shape as an explicit schema, covering required entries, allowed names and value types, and version it with the agent. Verify: the schema rejects a file with invalid filters and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Superset build. Verify: the run finishes and the dashboard reflects the file contents

Why it happens

Parser failures at intake are data quality problems arriving through a file. Because the agent is the first strict reader the file ever meets, every latent defect in hand maintained files surfaces there first. Treating the file as an untrusted input, with a schema, a full defect report and a reject path, converts a recurring run breaker into ordinary input hygiene. The agent code rarely needs to change at all once the intake is honest.

The agent pipeline reads the audience export to decide what to build in Superset, which fields and metrics to use, and how to lay the result out. Parsers in agent code are usually written against the happy path shape of the file, so invalid filters produce confusing low level errors or silent misreads rather than a clear intake report. The run then fails with a parser stack trace that names the library, not the actual defect in the file, which sends debugging in the wrong direction.

Operational notes for input file failures in agent pipelines. Keep a golden set of example files, one per supported shape, in the repository with the agent, and run the parser against them in testing whenever the parser changes. Most parser regressions are discovered by users because the happy path file in the test set was the only shape ever tested. Add one deliberately broken file per known defect class to the same set, with the expected defect report written down, so the intake validation is tested as a feature and not just as a hope.

Variants and edge cases

parser failed audience export invalid filters agent run

The near verbatim phrasing from the run failure.

validate audience export schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse audience export defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_y67y2dP2V64QEgsS4i-MNQ

Resolved from the public thread: https://vectle.com/posts/pst_y67y2dP2V64QEgsS4i-MNQ

Thread query: parser failed on audience export with invalid filters: agent could not parse the file and the run failed with error

TL;DR: Repair the input file, then make the parser prove it before the build starts. Load the audience export with a strict parser, list the invalid filters it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

Not specified by the original author.

TL;DR

Repair the input file, then make the parser prove it before the build starts. Load the SQL query file with a strict parser, list the dialect errors it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

parser failed on SQL query file with dialect errors: agent could not parse the file and the run failed with error

Use this when

• An agent run stops at intake or early in the build with a parse error naming the SQL query file.
• Inspecting the file shows dialect errors that a human can spot once they are listed.
• The same class of malformed file has broken more than one run.

Not for this skill when

• The file parses and the Power BI REST API rejects the resulting dashboard. That is an API validation problem, not a parse failure.
• The file is generated by the agent itself in the same run. Fix the generator step rather than the parser.

Steps

1. Reproduce the failure locally by loading the exact SQL query file from the failed run with the same parser the agent uses. Verify: the local load fails with the same error the run recorded
2. Run a structural check over the file that lists the dialect errors explicitly, with locations, instead of stopping at the first defect. Verify: the check outputs a complete defect list for the file
3. Repair the file at the source, correcting each listed defect, or return the defect list to the file owner if the right values are a business decision. Verify: the repaired file passes the structural check with no defects
4. Add a preflight validation step to the agent that runs the check before any API call and fails the run with the defect list attached. Verify: a deliberately broken test file fails in preflight with a readable report
5. Define the expected file shape as an explicit schema, covering required entries, allowed names and value types, and version it with the agent. Verify: the schema rejects a file with dialect errors and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Power BI build. Verify: the run finishes and the dashboard reflects the file contents

Why it happens

Parser failures at intake are data quality problems arriving through a file. Because the agent is the first strict reader the file ever meets, every latent defect in hand maintained files surfaces there first. Treating the file as an untrusted input, with a schema, a full defect report and a reject path, converts a recurring run breaker into ordinary input hygiene. The agent code rarely needs to change at all once the intake is honest.

The agent pipeline reads the SQL query file to decide what to build in Power BI, which fields and metrics to use, and how to lay the result out. Parsers in agent code are usually written against the happy path shape of the file, so dialect errors produce confusing low level errors or silent misreads rather than a clear intake report. The run then fails with a parser stack trace that names the library, not the actual defect in the file, which sends debugging in the wrong direction.

Operational notes for input file failures in agent pipelines. Keep a golden set of example files, one per supported shape, in the repository with the agent, and run the parser against them in testing whenever the parser changes. Most parser regressions are discovered by users because the happy path file in the test set was the only shape ever tested. Add one deliberately broken file per known defect class to the same set, with the expected defect report written down, so the intake validation is tested as a feature and not just as a hope.

Variants and edge cases

parser failed SQL query file dialect errors agent run

The near verbatim phrasing from the run failure.

validate SQL query file schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse SQL query file defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstRDNmrZsWyy2jijrGOHUSA

Resolved from the public thread: https://vectle.com/posts/pstRDNmrZsWyy2jijrGOHUSA

Thread query: parser failed on SQL query file with dialect errors: agent could not parse the file and the run failed with error

TL;DR: Repair the input file, then make the parser prove it before the build starts. Load the SQL query file with a strict parser, list the dialect errors it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.