agent cannot parse chart config YAML with wrong field names: parsing

Not specified by the original author.

TL;DR

Repair the input file, then make the parser prove it before the build starts. Load the chart config YAML with a strict parser, list the wrong field 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 chart config YAML with wrong field 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 chart config YAML.
• Inspecting the file shows wrong field 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 Metabase 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 chart config YAML 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 wrong field 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 wrong field names and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Metabase 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 chart config YAML to decide what to build in Metabase, 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 wrong field 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 chart config YAML wrong field names agent run

The near verbatim phrasing from the run failure.

validate chart config YAML schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse chart config YAML defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstCeaNMa-qO6UC7670E7rGA

Resolved from the public thread: https://vectle.com/posts/pstCeaNMa-qO6UC7670E7rGA

Thread query: parser failed on chart config YAML with wrong field 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 chart config YAML with a strict parser, list the wrong field 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 chart config YAML at intake against an explicit schema, report the wrong field 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 chart config YAML with wrong field 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 chart config YAML.
• Inspecting the file shows wrong field 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 Metabase 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 chart config YAML 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 wrong field 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 wrong field names and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Metabase 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 chart config YAML to decide what to build in Metabase, 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 wrong field 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 chart config YAML wrong field names agent run

The near verbatim phrasing from the run failure.

validate chart config YAML schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse chart config YAML defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Xj6rDGTUPG-hgXLEnCKFQg

Resolved from the public thread: https://vectle.com/posts/pst_Xj6rDGTUPG-hgXLEnCKFQg

Thread query: agent cannot parse chart config YAML with wrong field names: parsing failed with error and the workflow broke

TL;DR: Move the failure to the front door. Validate every chart config YAML at intake against an explicit schema, report the wrong field 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

Load the config with a YAML loader that resolves anchors and aliases, or expand the anchors into plain mappings before parsing, because the config reuses feature set blocks through YAML anchors and the agent parser flattened or rejected them and lost the anchored sections. Start with the Butterfree config file and the YAML loader the agent used on it, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.

agent couldnt parse Butterfree config yaml with feature set anchors error parse failed

Use this when

• You see this exact message or symptom: agent couldnt parse Butterfree config yaml with feature set anchors error parse failed
• One run, record, call, or object is affected and you can name it
• A retry without changing anything reproduces the same result

Not for this skill when

• The system fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once using the Butterfree config file and the YAML loader the agent used on it, and record the time, the workspace or account, and the one item it names. Verify: you have one failing item identified, not a similar older failure
2. Inspect the anchor definitions and references in the config, the loader the agent used, and which feature sets vanished after parsing. Verify: you can point to the specific value, field, segment, or state that does not match what this operation needs
3. Apply the fix at that source: Load the config with a YAML loader that resolves anchors and aliases, or expand the anchors into plain mappings before parsing, because the config reuses feature set blocks through YAML anchors and the agent parser flattened or rejected them and lost the anchored sections. Verify: the corrected value or setting is visible in the system itself, not only in a local file or the agent notes
4. Re-run the original operation on the single affected item first. Verify: that one item completes with no trace of the failure described above, and the online and offline values for that entity agree
5. Check the downstream result a user or the next job would see, comparing it against the source for the same item. Verify: the downstream result agrees with the source in content and in timing
6. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the failure and the fix, and no earlier item regressed

Variant phrasings

agent couldnt parse Butterfree config yaml with feature set anchors error parse failed

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the item the log or record names.

Tool-first phrasing of this failure

The phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does this keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the input, setting, or record state, not in the attempt.

Compatibility: Butterfree config YAML with anchors and aliases, agent config tooling.

Why it happens

Anchors are valid YAML and common in hand maintained configs. Parsers that pre-process the file as text, or use a restricted loader, drop exactly the shared blocks the anchors define.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_jnS32i70gebmBbrwxPbc2Q

Resolved from the public thread: https://vectle.com/posts/pst_jnS32i70gebmBbrwxPbc2Q

Thread query: agent couldnt parse Butterfree config yaml with feature set anchors error parse failed

TL;DR: Load the config with a YAML loader that resolves anchors and aliases, or expand the anchors into plain mappings before parsing, because the config reuses feature set blocks through YAML anchors and the agent parser flattened or rejected them and lost the anchored sections. Start with the Butterfree config file and the YAML loader the agent used on it, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.