{"uid":"cap_AW2GY7zHW8vCiJxg57Q6z","slug":"sampling-observations-record-quality-audit-string-normalisation-0318a06f","name":"Sampling Observations — Record Quality Audit + String Normalisation","description":"Sampling observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied sampling observations records (sample_value, sample_weight). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-001?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}}},"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_dHuK8kUx58aQiQz9mtTbe","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 data quality and normalises strings in sampling observation records (sample_value, sample_weight) for up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on my sampling observations? I have about 150 rows with row IDs, dataset groups, dates, sample values, and sample weights — I want two reports flagging any quality issues and normalising the strings.","exampleUseCases":[{"title":"Pre-analysis sampling data cleanup","prompt":"I've got 120 rows of survey sampling data with sample_value and sample_weight columns and I need to check them for quality issues and normalise the strings before I run my weighted analysis — can you audit them and give me two reports?"},{"title":"Detecting bad records in weighted observations","prompt":"Before I import this batch of 80 sampling observations into my pipeline, can you run a record quality audit on the sample_value and sample_weight fields and also normalise any messy strings in the dataset_group names?"},{"title":"Validating grouped sample data by date","prompt":"I have sampling observation records across several dataset groups from October 2026, around 50 rows, and I need a quality audit plus string normalisation report to catch any nulls, outliers, or formatting problems before sharing the results with my team."}],"resultDescription":"Two independent reports: (1) a record quality audit identifying issues such as nulls, out-of-range values, or malformed rows across the supplied sampling observations; (2) a string normalisation report detailing inconsistencies, whitespace problems, or casing issues in string fields like dataset_group. Both reports are generated locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected with maxItems validation error","Missing required fields (row_id, date, sample_value, sample_weight, dataset_group) — row-level validation failure","Date field not matching YYYY-MM-DD pattern — schema validation error","Numeric values outside allowed range (-1e12 to 1e12) — validation rejection","Empty rows array — minItems constraint failure","Payment not completed via x402 — request blocked"],"whenToPreferThis":"Choose this endpoint when you need a fast, local, two-report quality assessment of sampling observation records specifically containing sample_value and sample_weight fields, for up to 200 rows, with no need for live lookups, predictions, or external data enrichment. Prefer it over sibling endpoints when you only need record quality audit and string normalisation (without grouped numeric summary, deduplication, or correlation analysis).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:22.612Z","isFirstParty":false,"canonicalSlug":"sampling-observations-record-quality-audit-string-normalisation-0318a06f"}