{"uid":"cap_zH23UAOqgckcuFeIHljpi","slug":"sampling-observations-record-quality-audit-string-normalisation-iqr-a1fdce37","name":"Sampling Observations — Record Quality Audit + String Normalisation + IQR Outlier Flags","description":"Sampling observations — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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-15-203?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_dZB9g0sG8z5uP_y020E4j","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: a record quality audit, string normalisation, and IQR-based outlier flagging for sample_value or sample_weight fields.","exampleAgentPrompt":"Can you audit these 150 sampling observation rows for record quality issues, normalise the string fields, and flag any IQR outliers in the sample_value column? I'll paste in the rows with row_id, dataset_group, date, sample_value, and sample_weight.","exampleUseCases":[{"title":"Survey data quality pre-check","prompt":"I have 120 rows of survey sampling data with sample_value and sample_weight fields — can you run a record quality audit on them, normalise any messy string fields, and flag any IQR outliers in sample_value before I pass this to my analyst?"},{"title":"Outlier detection in weighted samples","prompt":"Here are 80 sampling observations across several dataset groups. Please flag which rows have IQR outliers in the sample_weight column and give me the quality audit and string normalisation reports too."},{"title":"Pre-analysis dataset validation","prompt":"Before I run my weighted average calculations, can you check these 200 sampling observation records for data quality issues, clean up the string fields, and identify any sample_value outliers using IQR?"}],"resultDescription":"Three bundled reports for up to 200 input rows: (1) a record quality audit identifying missing, malformed, or invalid field values; (2) string normalisation output cleaning dataset_group and other string fields; and (3) IQR outlier flags marking rows whose sample_value or sample_weight falls outside interquartile bounds. All processing is local — no external lookups or predictions are made.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, sample_value, sample_weight) causes row-level rejection","Dates not matching YYYY-MM-DD pattern are flagged or rejected","Values outside ±1,000,000,000,000 range are rejected by schema","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 sampling observation rows with sample_value and sample_weight fields and need all three of: record quality auditing, string normalisation, and IQR outlier detection in a single bundled call at low cost ($0.01 USDC). Prefer it over separate data-cleaning tools when your data is already structured in the expected schema and you want local, deterministic analytics without external dependencies or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:50.002Z","isFirstParty":false,"canonicalSlug":"sampling-observations-record-quality-audit-string-normalisation-iqr-a1fdce37"}