{"uid":"cap_qEdCCfUPQWkKAyeidIapb","slug":"sampling-observations-record-quality-audit-string-normalisation-b16e8516","name":"Sampling Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"Sampling observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied sampling observations records (sample_value, sample_weight). 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-15-200?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","sample_value","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"sample_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","sample_value":10,"dataset_group":"group-a","sample_weight":1},{"date":"2026-10-02","row_id":"row-2","sample_value":20,"dataset_group":"group-b","sample_weight":2},{"date":"2026-10-03","row_id":"row-3","sample_value":15,"dataset_group":"group-a","sample_weight":3},{"date":"2026-10-04","row_id":"row-4","sample_value":30,"dataset_group":"group-b","sample_weight":4}],"maxItems":200,"minItems":1},"field":{"enum":["sample_value","sample_weight"],"type":"string","default":"sample_value"}}},"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_KnRo-vFX3bU15uuBQlU9d","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 local analytical reports on up to 200 sampling observation rows: record quality audit, string normalisation, and adjacent row change detection for sample_value or sample_weight.","exampleAgentPrompt":"Can you run all three reports — record quality audit, string normalisation, and adjacent row changes — on my sampling observations? Here are up to 200 rows with row_id, dataset_group, date, sample_value, and sample_weight fields; I want to check the sample_value column.","exampleUseCases":[{"title":"Survey data quality pre-check","prompt":"Before I analyse my survey results, can you audit record quality, check string normalisation, and flag adjacent row changes for these 150 sampling observation rows? The field I care about is sample_value."},{"title":"Weighted sample anomaly detection","prompt":"I've got 80 rows of weighted sample observations and I want to know if there are any quality issues, string inconsistencies in the dataset_group labels, and sudden jumps between consecutive sample_weight values — can you run all three reports?"},{"title":"Time-series observation consistency review","prompt":"These 200 daily sampling records go from 2025-01-01 to 2025-07-20 — can you do a record quality audit, normalise the string fields, and show me where adjacent rows have big changes in sample_value?"}],"resultDescription":"Returns three independent analytical reports generated locally from the supplied rows: (1) a record quality audit flagging malformed, missing, or out-of-range values; (2) a string normalisation report identifying inconsistent casing, whitespace, or formatting in text fields like dataset_group; and (3) an adjacent row changes report showing where consecutive rows differ significantly in the selected numeric field (sample_value or sample_weight). No external data is fetched.","failureModes":["Fewer than 1 or more than 200 rows supplied — request rejected","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) on any row — validation error","Invalid date format (must match YYYY-MM-DD) — pattern validation failure","Field selector not one of 'sample_value' or 'sample_weight' — enum error","Payment not supplied or insufficient — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need three bundled local analytical reports (quality audit, string normalisation, adjacent row changes) on sampling observation data in a single low-cost call, without requiring any external lookups, predictions, or live data. It is ideal for pre-analysis data validation pipelines where sample_value and sample_weight are the core numeric fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:52.698Z","isFirstParty":false,"canonicalSlug":"sampling-observations-record-quality-audit-string-normalisation-b16e8516"}