{"uid":"cap_csoX_kEo-QKZlq68eMpa1","slug":"data-ingestion-batches-record-quality-audit-string-normalisation-iqr-95130c0f","name":"Data Ingestion Batches — Record Quality Audit + String Normalisation + IQR Outlier Flags","description":"Data ingestion batches — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-203?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_rows"}}},"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_NL4Z-H2Jr6qyIMiYlbxKs","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":"Runs three analytical reports on data ingestion batch records: a record quality audit, string normalisation, and IQR-based outlier flagging for received_rows and accepted_rows fields.","exampleAgentPrompt":"Run a record quality audit, string normalisation, and IQR outlier check on these ingestion batch rows — the dataset has fields like row_id, dataset_group, date, received_rows, and accepted_rows, and I want to analyse the received_rows field across up to 50 records.","exampleUseCases":[{"title":"ETL pipeline quality monitoring","prompt":"I have a batch of 80 data ingestion records with received_rows and accepted_rows counts per dataset group and date — can you run all three reports (quality audit, string normalisation, and IQR outlier flags) on the received_rows field so I can spot any anomalies?"},{"title":"Detecting outlier ingestion volumes","prompt":"We had some weird spikes in our accepted_rows counts last week across different dataset groups — can you flag the outliers using IQR analysis on these 120 ingestion batch rows?"},{"title":"Cleaning dataset group string labels","prompt":"Our ingestion pipeline logs have inconsistent dataset_group labels in the batch records — can you run the string normalisation report on these rows so we can see what needs cleaning?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit highlighting completeness and validity issues across the supplied rows, (2) a string normalisation report on text fields like dataset_group, and (3) IQR-based outlier flags identifying statistically anomalous values in the selected numeric field (received_rows or accepted_rows). Up to 200 rows processed per call.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Field selector not one of 'received_rows' or 'accepted_rows' — enum validation error","Empty rows array — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you need fast, offline analytical reporting on data ingestion batch records without any external lookups or live data. It is ideal for pipelines where you want three bundled reports (quality audit, string normalisation, IQR outlier detection) in a single low-cost call. Prefer it over single-purpose analytics tools when your data is already in hand and you want comprehensive local analysis at $0.01 per call for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T00:52:26.790Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-record-quality-audit-string-normalisation-iqr-95130c0f"}