{"uid":"cap_WxSbIcwUWS8W5t5LiCUSs","slug":"data-ingestion-batch-analyzer-quality-audit-string-normalization-13e16891","name":"Data Ingestion Batch Analyzer — Quality Audit, String Normalization & Deduplication","description":"Data ingestion batches — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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-01-191?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}}},"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_zlUBddECOp2BdLpn_OIaY","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 up to 200 data ingestion batch rows: a record quality audit, string normalization, and exact row deduplication — all using local engines with no external lookups.","exampleAgentPrompt":"Can you run a quality audit, string normalization, and deduplication check on my ingestion batch data? I have rows like: row-1 on 2026-10-01 in group-a with 10 received and 1 accepted, row-2 on 2026-10-02 in group-b with 20 received and 2 accepted — up to 10 rows total.","exampleUseCases":[{"title":"ETL pipeline batch quality review","prompt":"I have a week's worth of ingestion batch records across two dataset groups — can you run all three reports: quality audit, string normalization, and exact deduplication on them? Here are the rows: row-1 (2026-10-01, group-a, received 500, accepted 480), row-2 (2026-10-02, group-a, received 300, accepted 300), row-3 (2026-10-02, group-a, received 300, accepted 300) — that last one might be a duplicate."},{"title":"Accepted vs received row anomaly detection","prompt":"Can you audit the quality of these ingestion batch records and flag anything suspicious about the accepted vs received row counts? My rows are: row-1 (2026-11-01, group-x, received 1000, accepted 5), row-2 (2026-11-02, group-x, received null, accepted 200), row-3 (2026-11-03, group-y, received 50, accepted 60)."},{"title":"Multi-group ingestion string normalization","prompt":"I've got ingestion records across several dataset groups with inconsistent string casing and formatting — please run the string normalization and deduplication reports on these 5 rows: row-A (2026-09-15, Group Alpha, received 100, accepted 90), row-B (2026-09-15, group alpha, received 100, accepted 90), row-C (2026-09-16, GROUP BETA, received 200, accepted 180), row-D (2026-09-16, group beta, received 200, accepted 180), row-E (2026-09-17, Group Alpha, received 150, accepted 140)."}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, or suspicious accepted/received ratios; (2) a string normalization report identifying and correcting inconsistencies in text fields like dataset_group; (3) an exact row deduplication report identifying and removing identical rows. All computed locally with no external calls, covering up to 200 input rows.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) causes rejection","Malformed date strings not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 range are rejected","Empty rows array returns an error","Payment failure via x402 protocol results in no report being returned"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, affordable ($0.01 USDC) triple-report data quality pass on ingestion batch records without requiring an external data warehouse, live API, or heavyweight ETL tool. It is ideal for spot-checking ingestion pipelines, validating batch uploads before further processing, or automating periodic data quality audits on small-to-medium row sets (up to 200 rows). Prefer this over custom scripts when you need all three capabilities — quality audit, normalization, and deduplication — bundled in a single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T18:48:26.651Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batch-analyzer-quality-audit-string-normalization-13e16891"}