{"uid":"cap_D3ChEQyK722gM8aS-1aPA","slug":"time-series-completeness-record-quality-audit-duplicate-key-report-62ccc996","name":"Time Series Completeness — Record Quality Audit & Duplicate Key Report","description":"Time series completeness — Record quality audit + Duplicate key report: Record quality audit + Duplicate key report for supplied time series completeness records (observed_points, expected_points). 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-16-003?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}}},"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_70YYLN-Ouc8eI-DR9jYu7","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 a record quality audit and duplicate key detection report on up to 200 supplied time series completeness rows (observed_points, expected_points), returning two independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and duplicate key check on my time series completeness data? I have rows for dataset groups 'sensor-a' and 'sensor-b' from 2025-01-01 to 2025-01-10 with observed_points and expected_points values — flag any quality issues and duplicate keys across up to 50 rows.","exampleUseCases":[{"title":"IoT sensor data completeness audit","prompt":"I have 80 rows of IoT sensor time series data with observed_points and expected_points per day for groups 'temp-sensor' and 'humidity-sensor' from 2025-03-01 through 2025-04-20 — can you run a full record quality audit and flag any duplicate row IDs or date-group combinations?"},{"title":"Financial reporting data integrity check","prompt":"Before I finalize my monthly report, can you audit these 150 time series completeness records for our 'revenue' and 'expense' dataset groups across Q1 2025 — I need a quality audit and a duplicate key report to catch any bad or repeated rows?"},{"title":"ETL pipeline output validation","prompt":"Our ETL pipeline just produced 200 time series rows with observed_points and expected_points for dataset groups 'pipeline-run-A' and 'pipeline-run-B' — can you check the data quality and identify any duplicate keys before we load it into the warehouse?"}],"resultDescription":"Returns two independent reports: (1) a record quality audit highlighting issues such as null values, out-of-range numbers, or malformed fields across the supplied rows, and (2) a duplicate key report identifying rows sharing the same key combinations (e.g. row_id, date, dataset_group). Both reports are produced locally without external lookups.","failureModes":["Exceeding 200 rows causes rejection — only up to 200 rows accepted per call","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors","Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation","Empty rows array (minItems: 1 not satisfied) will be rejected","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) will fail schema validation"],"whenToPreferThis":"Choose this endpoint when you need a fast, local, low-cost ($0.01 USDC) dual-report — quality audit plus duplicate key detection — on batches of up to 200 time series completeness records with observed and expected point counts, without requiring any live data lookups, predictions, or external integrations. Ideal for pre-load validation in ETL pipelines, periodic data hygiene checks, or auditing completeness datasets before downstream analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:02.740Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-duplicate-key-report-62ccc996"}