{"uid":"cap_7V9oXH0BQa6iCFVBCSLgk","slug":"training-example-metadata-invalid-row-export-72ceff38","name":"Training Example Metadata — Invalid Row Export","description":"Training example metadata — Invalid row export: Invalid row export 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 invalid row export report.","url":"https://market.datapackvibe.com/x402/assistant-bx-csv-training-data-invalid-rows?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"csv":{"type":"string","default":"example_id,created_on,label,text_length,sample_weight\nrecord-a,2026-01-01,group-a,10,2\nrecord-b,2026-01-02,group-b,20,3","maxLength":100000,"minLength":1},"maximum":{"type":"number","default":1000000000000,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":-1000000000000,"maximum":1000000000000,"minimum":-1000000000000},"delimiter":{"enum":[",",";","\t"],"type":"string","default":","},"max_cell_length":{"type":"integer","default":1000,"maximum":5000,"minimum":1},"allowed_categories":{"type":"array","items":{"type":"string","default":"","maxLength":100,"minLength":1},"default":["group-a","group-b"],"maxItems":100,"minItems":0}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_pMy5FfYUzxKrs3Gx5dHci","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Validates a CSV of training example metadata and returns a report of all rows that violate declared constraints (value ranges, category allowlists, cell length limits).","exampleAgentPrompt":"Check my training example CSV for invalid rows — use comma as the delimiter, only allow categories 'group-a' and 'group-b', keep cell length under 500, and flag any numeric values outside -1000 to 1000. Here's the CSV: example_id,created_on,label,text_length,sample_weight\\nrecord-a,2026-01-01,group-a,10,2\\nrecord-b,2026-01-02,group-b,20,3","exampleUseCases":[{"title":"Pre-training data quality audit","prompt":"I have a CSV of training examples with columns example_id, label, and sample_weight. Can you validate it and export a report of any rows where label isn't in 'cat' or 'dog', sample_weight is outside 0 to 10, or any cell is longer than 200 characters? Delimiter is comma."},{"title":"ML pipeline bad-row detection","prompt":"Before I kick off model training, can you scan this CSV (tab-delimited) for invalid rows? Allowed categories are 'positive', 'negative', and 'neutral', numeric values should be between -5 and 5, and max cell length is 1000."},{"title":"Dataset labeling error report","prompt":"My annotation team just delivered a CSV of labeled training data separated by semicolons. Can you find all the rows that have disallowed labels — the only valid ones are 'spam' and 'not-spam' — or that have any number outside 0 to 100, and give me the invalid row export?"}],"resultDescription":"Returns a structured report listing every row in the input CSV that violates the declared constraints, including which rows failed (by row index or identifier), the nature of the violation (out-of-range numeric value, disallowed category, exceeded cell length), and a summary count of invalid rows. Up to 1000 rows are evaluated per call.","failureModes":["CSV exceeds 100,000 character limit — request rejected","CSV has fewer than 1 character — empty input error","delimiter not one of comma, semicolon, or tab — validation error","more than 100 allowed_categories supplied — schema rejection","max_cell_length outside 1–5000 range — parameter error","numeric minimum greater than maximum — logical constraint error","malformed CSV (e.g. unmatched quotes) — parse error returned"],"whenToPreferThis":"Choose this endpoint when you need to validate a CSV file of training example metadata against explicit, declared constraints (numeric ranges, category allowlists, cell length caps) and receive an actionable report of only the offending rows. It is purpose-built for ML training data quality checks and handles quoted cells correctly. Prefer it over generic CSV validators when you need the combination of category allowlist enforcement, numeric bounds checking, and per-cell length limits in a single call, and when your dataset is up to 1000 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T06:51:04.154Z","isFirstParty":false,"canonicalSlug":"training-example-metadata-invalid-row-export-72ceff38"}