{"uid":"cap_sxM6FjNpuMja9PNoE62YQ","slug":"training-example-metadata-numeric-type-check-1339c0b0","name":"Training Example Metadata — Numeric Type Check","description":"Training example metadata — Numeric type check: Numeric type check 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 numeric type check report.","url":"https://market.datapackvibe.com/x402/assistant-bx-csv-training-data-types?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_rx8lnW-S1cDnx0Bv5CPzL","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 that numeric columns in a training-data CSV contain only valid numbers within specified bounds and allowed categories.","exampleAgentPrompt":"Can you run a numeric type check on this training data CSV — comma-delimited, numbers should be between 0 and 1000000, max cell length 500 — and tell me if there are any invalid or out-of-range values?","exampleUseCases":[{"title":"Pre-training dataset quality gate","prompt":"Before I kick off model training, can you validate this CSV training file to confirm all numeric columns like text_length and sample_weight contain valid numbers between -1000000000000 and 1000000000000, using comma as the delimiter?"},{"title":"CI pipeline CSV contract check","prompt":"I need to verify our exported training CSV passes a numeric type contract — delimiter is comma, max cell length 1000, and allowed category values are group-a and group-b. Can you run the check and flag any rows that fail?"},{"title":"Data cleaning audit before fine-tuning","prompt":"We just received a new batch of labeled training examples as a CSV and I want to make sure all the numeric fields are clean — can you check the file for non-numeric values or numbers outside the range -100 to 100, with tab as the delimiter?"}],"resultDescription":"A structured CSV numeric type check report indicating which rows and cells contain invalid or non-numeric values, out-of-range numbers, or cells exceeding the maximum length, along with a pass/fail summary for the declared contract.","failureModes":["CSV exceeds 100,000 character limit — request rejected","CSV has zero rows or is empty — validation cannot proceed","Delimiter mismatch causes all cells to be unparsed","minimum value exceeds maximum value — invalid range configuration","Cell values exceed max_cell_length and are flagged or truncated","Malformed CSV with unclosed quotes may produce unexpected parse results"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, contract-based numeric type validation on a training data CSV — particularly when you want to enforce value ranges and allowed category labels in a single pass without external enrichment. It is purpose-built for ML training metadata validation (up to 1000 rows) and is more cost-effective than a full schema validation service when only numeric type correctness is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T06:50:41.420Z","isFirstParty":false,"canonicalSlug":"training-example-metadata-numeric-type-check-1339c0b0"}