{"uid":"cap_nw7oKnKAdrf2Fu8mSij6Z","slug":"sampling-observations-data-quality-processor-56548d83","name":"Sampling Observations Data Quality Processor","description":"Sampling observations — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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-15-208?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_c22qD2RWllfa2nFoPYpdd","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 record quality, normalises strings, and fills missing values for sampling observations datasets (sample_value, sample_weight) — up to 200 rows, 3 reports per call.","exampleAgentPrompt":"Can you audit and clean my sampling observations dataset — run a record quality audit, string normalisation, and fill any missing sample_value fields using median imputation? I have about 50 rows across two groups, group-a and group-b, dated from 2026-10-01.","exampleUseCases":[{"title":"Pre-analysis missing value imputation","prompt":"I have 120 sampling observation rows with some null sample_weight values — can you fill those missing weights using mean imputation and give me the quality audit report before I run my weighted analysis?"},{"title":"Survey data string normalisation check","prompt":"My survey sampling dataset has inconsistent text in the dataset_group field across 80 rows — can you run string normalisation and a record quality audit on it so I know what needs fixing?"},{"title":"Batch data validation before pipeline ingestion","prompt":"Before I load these 200 sampling observation records into our data pipeline, can you do a full record quality audit and flag any missing or malformed values in sample_value and sample_weight — use zero fill for any missing values?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging malformed, missing, or out-of-range values per row; (2) a string normalisation report for text fields like dataset_group and date; and (3) a missing-value fill report showing which sample_value or sample_weight cells were null and what value was imputed using the chosen method (zero, mean, or median). All processing is local with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Invalid date format (non-YYYY-MM-DD) triggers schema rejection","sample_value or sample_weight outside ±1,000,000,000,000 range fails validation","Missing required fields (row_id, date, dataset_group) causes row-level rejection","Invalid method enum value (not zero/mean/median) returns a bad request error","Invalid field enum value (not sample_value/sample_weight) returns a bad request error"],"whenToPreferThis":"Choose this endpoint when you need three combined data-quality operations — audit, string normalisation, and missing-value imputation — on sampling observation records in a single $0.01 call. It is ideal for pre-processing tabular survey or sampling datasets before downstream analysis, when you want offline/local processing with no external API dependencies, and when your dataset fits within 200 rows. Prefer alternatives if you need predictive modelling, live data lookup, or operations on non-sampling entity types like business proposals or retail figures.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:51.032Z","isFirstParty":false,"canonicalSlug":"sampling-observations-data-quality-processor-56548d83"}