{"uid":"cap_RpyBtMsPHwQyBkVxyFKe5","slug":"sampling-observations-analytics-quality-audit-string-normalisation-304efcbd","name":"Sampling Observations Analytics — Quality Audit, String Normalisation & Numeric Row Ordering","description":"Sampling observations — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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-15-196?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_shSSp-663mwNrhAKl-fqo","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":"Performs three local analytical reports on sampling observation records: record quality audit, string normalisation, and numeric row ordering by sample_value or sample_weight.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and numeric row ordering report on my sampling observations? Here are my rows with row IDs, dataset groups, dates, sample values, and sample weights — up to 200 rows — and I want the analysis ordered by sample_value.","exampleUseCases":[{"title":"Survey data quality check","prompt":"I have 150 survey sampling records with row IDs, group labels, dates, response values, and weights — can you audit their quality, normalise the string fields, and sort them by sample_value so I can see which entries are most reliable?"},{"title":"Lab sample dataset validation","prompt":"Please run a quality audit and string normalisation on my lab sampling observations dataset — it has 80 rows with sample_value and sample_weight columns — and also give me the rows ordered by sample_weight."},{"title":"Marketing panel observation hygiene","prompt":"I've got a marketing panel dataset with around 120 sampling rows including group names, dates, sample values, and weights. Can you check data quality, clean up the string fields, and rank the rows numerically by sample_value?"}],"resultDescription":"Returns three independent analytical reports for the supplied rows: (1) a record quality audit flagging completeness, format, and consistency issues; (2) a string normalisation report standardising text fields like dataset_group; (3) a numeric row ordering report ranking rows by the selected field (sample_value or sample_weight). All analysis is performed locally with no external lookups.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, sample_value, sample_weight) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers schema rejection","Numeric values outside ±1,000,000,000,000 range are rejected","Empty rows array returns an error","Unsupported field enum value causes request failure"],"whenToPreferThis":"Choose this endpoint when you need a combined, low-cost batch analytical pass over up to 200 sampling observation records covering quality auditing, string normalisation, and numeric ordering in a single call. It is ideal for offline/local data hygiene workflows with no need for external data lookups, predictions, or professional assessments. At $0.01 USDC for three reports, it is cost-effective for routine data validation pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:58.706Z","isFirstParty":false,"canonicalSlug":"sampling-observations-analytics-quality-audit-string-normalisation-304efcbd"}