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Training Example Metadata CSV Import Audit
x402
Training Example Metadata CSV Import Audit 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 file representing training example metadata against a declared table contract, checking up to 1000 rows for structural and content conformance.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training example metadata — Complete import audit: Complete import audit 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 complete import audit report.
Output
Returns a complete import audit report detailing whether the CSV conforms to the declared contract, including row-level findings, category violations, numeric range breaches, cell length failures, and an overall pass/fail status for up to 1000 rows.
Example use cases
ML dataset label compliance check
“I have a CSV of training examples and I need to make sure every value in the 'label' column is one of 'cat', 'dog', or 'bird' — can you run a complete import audit on it using comma as the delimiter and flag any rows that violate that?”
Training data cell length enforcement
“Before I load this training CSV into my pipeline, can you audit it to confirm no individual cell exceeds 200 characters and all numeric values are between 0 and 1000? The file uses a comma delimiter.”
Tab-delimited training file validation
“My training dataset is tab-separated and I need a full import audit — please validate it against allowed categories 'positive' and 'negative', with a max cell length of 300, and let me know if anything fails the contract.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-csv-training-data-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate a CSV file of training examples against a strict declared schema contract before ingesting into an ML pipeline, particularly when you need to enforce allowed categorical label values, numeric range bounds, and per-cell length limits. It is purpose-built for training data metadata CSVs (with fields like example_id, label, sample_weight) and provides a structured audit report rather than simple parsing. Prefer this over generic CSV validators when label conformance and ML-domain constraints are primary concerns.
Side effects
- CSV exceeds 100,000 character limit — request rejected
- CSV contains fewer than 1 character — empty input error
- Delimiter not one of comma, semicolon, or tab — validation error
- Rows exceed 1000 — only first 1000 may be audited
- max_cell_length outside 1–5000 range — parameter error
- allowed_categories list exceeds 100 items — parameter error
- Malformed JSON body — 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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