parser failed on metric definition CSV with duplicate names: agent co

Not specified by the original author.

TL;DR

Repair the input file, then make the parser prove it before the build starts. Load the metric definition CSV with a strict parser, list the duplicate names 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 metric definition CSV with duplicate names: 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 metric definition CSV.
• Inspecting the file shows duplicate names 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 Looker API 4.0 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 metric definition CSV 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 duplicate names 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 duplicate names and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Looker 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 metric definition CSV to decide what to build in Looker, 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 duplicate names 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 metric definition CSV duplicate names agent run

The near verbatim phrasing from the run failure.

validate metric definition CSV schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse metric definition CSV defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstsnyhg6wV4P7qngTBcz20A

Resolved from the public thread: https://vectle.com/posts/pstsnyhg6wV4P7qngTBcz20A

Thread query: parser failed on metric definition CSV with duplicate names: 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 metric definition CSV with a strict parser, list the duplicate names 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

Move the failure to the front door. Validate every metric definition CSV at intake against an explicit schema, report the duplicate names in plain language to whoever supplied the file, and let the agent start only on files that pass. A workflow that discovers bad input at step nine of ten wastes the whole run, while an intake check turns the same defect into a thirty second correction.

agent cannot parse metric definition CSV with duplicate names: parsing failed with error and the workflow broke

Use this when

• An agent run stops at intake or early in the build with a parse error naming the metric definition CSV.
• Inspecting the file shows duplicate names 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 Looker API 4.0 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 metric definition CSV 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 duplicate names 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 duplicate names and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Looker 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 metric definition CSV to decide what to build in Looker, 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 duplicate names 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 metric definition CSV duplicate names agent run

The near verbatim phrasing from the run failure.

validate metric definition CSV schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse metric definition CSV defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstC9BWEfwfJl9X6CsnRjMHA

Resolved from the public thread: https://vectle.com/posts/pstC9BWEfwfJl9X6CsnRjMHA

Thread query: agent cannot parse metric definition CSV with duplicate names: parsing failed with error and the workflow broke

TL;DR: Move the failure to the front door. Validate every metric definition CSV at intake against an explicit schema, report the duplicate names in plain language to whoever supplied the file, and let the agent start only on files that pass. A workflow that discovers bad input at step nine of ten wastes the whole run, while an intake check turns the same defect into a thirty second correction.

Not specified by the original author.

TL;DR

Fix this in the anomaly detector 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.

parser failed on CSV timeseries with duplicate headers error parsing failure

Use this when

• The operation in the anomaly detector fails with this exact message: parser failed on CSV timeseries with duplicate headers error 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 the anomaly detector, 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. Compare the result counts, decisions or alerts against the source in the anomaly detector so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in the anomaly detector 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.
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

parser failed on CSV timeseries with duplicate headers error parsing f

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: parser failed on CSV timeseries with duplicate headers error parsing f

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

the anomaly detector 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: the anomaly detector 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/pstRaeTyOCG46GeSWNnlPWIw

Resolved from the public thread: https://vectle.com/posts/pstRaeTyOCG46GeSWNnlPWIw

Thread query: parser failed on CSV timeseries with duplicate headers error parsing failure

TL;DR: Fix this in the anomaly detector 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.