{"uid":"cap_c4EJ8zp3gFqz5AR-JIChb","slug":"time-series-completeness-analyzer-record-quality-audit-string-5fe485a9","name":"Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & Numeric Distribution","description":"Time series completeness — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution for supplied time series completeness records (observed_points, expected_points). 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-16-193?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"}}},"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_UYEClKwr7ySh2sJeMYQAG","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 analytical reports (record quality audit, string normalisation, and numeric distribution) on supplied time series completeness records containing observed and expected data points, for up to 200 rows.","exampleAgentPrompt":"Can you run a full quality audit, string normalisation, and numeric distribution analysis on my time series completeness records? Here are up to 200 rows with row_id, dataset_group, date, observed_points, and expected_points — I want all three reports focusing on the observed_points field.","exampleUseCases":[{"title":"Data pipeline completeness health check","prompt":"I have daily completeness records for two dataset groups over the past month — each row has a date, observed_points, and expected_points. Can you run the record quality audit, string normalisation, and numeric distribution reports on all of them so I can spot any data gaps or quality issues?"},{"title":"Numeric distribution profiling for expected points","prompt":"Run a numeric distribution analysis plus record quality audit on my time series completeness data focusing on the expected_points field — I want to understand the spread and catch any bad records across my dataset groups."},{"title":"Data quality review before ingestion","prompt":"Before I ingest these time series completeness records into our warehouse, can you audit them for quality issues, normalise the string fields like dataset_group, and show me the distribution of observed_points? I have about 150 rows with dates, row IDs, and both observed and expected point counts."}],"resultDescription":"Returns three independent analytical reports for the supplied rows: (1) a record quality audit flagging malformed, missing, or inconsistent records; (2) a string normalisation report for fields like dataset_group; and (3) a numeric distribution summary for the selected field (observed_points or expected_points), covering statistics such as min, max, mean, and spread — all computed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, observed_points, expected_points) cause row-level rejection","Invalid date format (not YYYY-MM-DD) fails pattern validation","Numeric values outside allowed range (-1e12 to 1e12) are rejected","Empty rows array is not accepted (minItems: 1)","Invalid field enum value (not observed_points or expected_points) causes a request error"],"whenToPreferThis":"Choose this endpoint when you need three simultaneous offline analytical reports — quality audit, string normalisation, and numeric distribution — specifically for time series completeness data structured around observed vs expected point counts. It is ideal for pre-ingestion data profiling, completeness monitoring, and batch quality checks on up to 200 rows. Prefer it over generic validators when you need bundled, multi-report analytics in a single low-cost call without any live data lookup or external dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:15.225Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analyzer-record-quality-audit-string-5fe485a9"}