{"uid":"cap_6Nw75CHLiqD2yGCBOwZQj","slug":"document-retrieval-record-quality-audit-string-normalisation-two-61fd3c24","name":"Document Retrieval Record Quality Audit + String Normalisation + Two-Measure Correlation","description":"Document retrieval — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied document retrieval records (retrieved_bytes, request_ms). 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-web-09-204?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","site_group","date","retrieved_bytes","request_ms"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"request_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"site_group":{"type":"string","default":"","maxLength":80},"retrieved_bytes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","request_ms":1,"site_group":"group-a","retrieved_bytes":10},{"date":"2026-10-02","row_id":"row-2","request_ms":2,"site_group":"group-b","retrieved_bytes":20},{"date":"2026-10-03","row_id":"row-3","request_ms":3,"site_group":"group-a","retrieved_bytes":15},{"date":"2026-10-04","row_id":"row-4","request_ms":4,"site_group":"group-b","retrieved_bytes":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_XVeout2a9jUtAdVLSAhtZ","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":"Audits quality, normalises strings, and computes correlation between retrieved_bytes and request_ms for up to 200 document retrieval records, returning three analytical reports.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and correlation analysis between retrieved_bytes and request_ms on my document retrieval data? Here are up to 200 rows with row_id, site_group, date, retrieved_bytes, and request_ms — I need all three reports back.","exampleUseCases":[{"title":"Audit web crawler retrieval log quality","prompt":"I have a batch of document retrieval logs from our web crawler — each row has a row_id, site_group, date, retrieved_bytes, and request_ms. Can you run a record quality audit, normalise the site_group strings, and check the correlation between retrieved_bytes and request_ms for me?"},{"title":"Detect latency-vs-size patterns in CDN logs","prompt":"We pulled 150 rows from our CDN retrieval records with fields row_id, site_group, date, retrieved_bytes, and request_ms. Please give me a quality audit of those records, normalise any messy string fields, and show me how correlated the byte sizes are with request latency."},{"title":"Validate data integrity before reporting","prompt":"Before I include our document retrieval metrics in the weekly report, can you check these rows for quality issues, clean up the site_group strings, and run a correlation between retrieved_bytes and request_ms? I have about 80 rows with row_id, date, site_group, retrieved_bytes, and request_ms."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing values, invalid formats, and anomalies; (2) a string normalisation report for the site_group field; and (3) a Pearson or similar two-measure correlation result between retrieved_bytes and request_ms — all computed locally without external lookups, covering up to 200 supplied rows.","failureModes":["Exceeds 200-row limit — request rejected or truncated","Missing required fields (row_id, date, retrieved_bytes, request_ms) cause row-level errors","Invalid date format (non YYYY-MM-DD) fails pattern validation","Numeric fields out of allowed range cause validation errors","Empty rows array returns an error","All-null numeric columns produce undefined correlation result"],"whenToPreferThis":"Choose this endpoint when you need simultaneous data quality auditing, string normalisation, and statistical correlation on document retrieval records (retrieved_bytes, request_ms) in a single low-cost call. It is ideal for batch validation of web retrieval logs before reporting or ingestion, when you do not need live data lookup, ML prediction, or external enrichment — just fast, local analytical computation on supplied tabular data up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:33:31.939Z","isFirstParty":false,"canonicalSlug":"document-retrieval-record-quality-audit-string-normalisation-two-61fd3c24"}