{"uid":"cap_wu_2YNO0HjBAAUiMweGun","slug":"null-coverage-string-normalisation-iqr-outlier-flags-68a78358","name":"Null Coverage String Normalisation + IQR Outlier Flags","description":"Null coverage — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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-03-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"}}},"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_FGW38wgdZtStI0h9vXNC8","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":"Normalises string fields and flags IQR-based outliers in null coverage records (missing_values, total_values) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier detection on these null coverage records? I have 4 rows: row-1 (group-a, 2026-10-01, missing=10, total=1), row-2 (group-b, 2026-10-02, missing=20, total=2), row-3 (group-a, 2026-10-03, missing=15, total=3), row-4 (group-b, 2026-10-04, missing=30, total=4) — flag outliers on the missing_values field.","exampleUseCases":[{"title":"Flagging outlier null rates in ETL pipeline","prompt":"I have daily ETL quality logs with missing_values and total_values per dataset group — can you normalise the strings and flag any IQR outliers in missing_values across these 12 rows so I know which dates had anomalous null rates?"},{"title":"Data audit across multiple dataset groups","prompt":"We're auditing completeness across group-a and group-b for October. Here are 50 rows of null coverage records — run the string normalisation and IQR outlier flags on total_values so we can spot which groups are statistically anomalous."},{"title":"Monitoring data quality regression after schema change","prompt":"After our schema migration last week, I want to check if any dataset groups spiked in missing values. Here are 30 rows of before-and-after null coverage data — can you normalise and apply IQR outlier detection on missing_values to surface the worst offenders?"}],"resultDescription":"Returns two analytical reports per call: (1) a string normalisation report for the dataset_group and related string fields, and (2) an IQR outlier flag report identifying which rows fall outside the interquartile range for the selected numeric field (missing_values or total_values). Covers up to 200 input rows with no external lookups.","failureModes":["Missing required fields (row_id, date, missing_values, total_values) causes validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Null or out-of-range numeric values may affect IQR computation accuracy","Invalid field enum value (not 'missing_values' or 'total_values') causes rejection","Empty rows array (minItems:1 violated) returns an error"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and IQR-based outlier detection on null coverage data (missing_values and total_values) in a single cheap call. It is ideal for data quality pipelines, ETL monitoring, and completeness auditing where you want statistical outlier flags without external lookups or ML predictions. Prefer it over generic stats APIs when your data is structured as dataset_group/date/missing_values/total_values records and you need two bundled local reports at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:21.016Z","isFirstParty":false,"canonicalSlug":"null-coverage-string-normalisation-iqr-outlier-flags-68a78358"}