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Training Example Metadata — Declared Column Projection
x402
Training Example Metadata — Declared Column Projection is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Validates a CSV training dataset against a declared column schema contract, checking up to 1000 rows for structure, value ranges, categories, and cell length.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training example metadata — Declared column projection: Declared column projection for Training example metadata. Validate a declared CSV table contract, up to 1000 rows. Quoted cells supported; no external enrichment. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one CSV declared column projection report.
Output
A declared column projection report for the submitted CSV training data, detailing whether each column conforms to its declared contract, including any out-of-range numeric values, disallowed category labels, cell length violations, and overall schema conformance status across up to 1000 rows.
Example use cases
Pre-training dataset schema audit
“I'm about to kick off a model training run — can you validate this CSV of training examples to make sure the declared columns all look right? It uses a comma delimiter, the label column should only contain 'positive' or 'negative', text_length should be between 0 and 10000, and no cell should exceed 500 characters.”
Category label integrity check
“Check my training data CSV for any rows where the label column has values outside the allowed set — I only want 'cat', 'dog', and 'bird' as valid categories. Use comma as delimiter and flag anything that doesn't conform to the declared column projection.”
Numeric range validation for sample weights
“Validate the sample_weight column in my training examples CSV — weights should all fall between 0.01 and 100.0, comma-separated, max cell length 200. Tell me which rows violate the declared range so I can fix them before training.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-csv-training-data-projection?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate a CSV file of ML training examples against a declared column schema contract, particularly when you need to enforce allowed categorical values, numeric min/max ranges, and cell length limits. Best suited for pre-training data quality checks on structured tabular training datasets up to 1000 rows. Prefer this over generic CSV validators when your use case is specifically training example metadata with declared column projections.
Side effects
- CSV exceeds 100,000 character limit — request rejected
- CSV has fewer than 1 character — empty input error
- More than 1000 rows submitted — only first 1000 processed or error returned
- Invalid delimiter specified outside of comma, semicolon, or tab — validation error
- allowed_categories list exceeds 100 items — schema error
- Cell length exceeds max_cell_length threshold — flagged in report
- Malformed CSV with unclosed quotes — parse error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csv | string | |
| maximum | number | |
| minimum | number | |
| delimiter | string | |
| max_cell_length | integer | |
| allowed_categories | array |
Tags
x402
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