{"uid":"cap_vvzzdKOzJ4treViMerX4k","slug":"pipeline-durations-analytics-record-quality-audit-string-68695640","name":"Pipeline Durations Analytics: Record Quality Audit + String Normalisation + Date by Category Pivot","description":"Pipeline durations — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied pipeline durations records (run_seconds, processed_rows). 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-05-206?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","run_seconds","processed_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"run_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","run_seconds":10,"dataset_group":"group-a","processed_rows":1},{"date":"2026-10-02","row_id":"row-2","run_seconds":20,"dataset_group":"group-b","processed_rows":2},{"date":"2026-10-03","row_id":"row-3","run_seconds":15,"dataset_group":"group-a","processed_rows":3},{"date":"2026-10-04","row_id":"row-4","run_seconds":30,"dataset_group":"group-b","processed_rows":4}],"maxItems":200,"minItems":1},"field":{"enum":["run_seconds","processed_rows"],"type":"string","default":"run_seconds"}}},"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_pqHF3kFKI27jUDxrSk6xa","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 date-by-category pivot) on supplied pipeline duration records containing run_seconds and processed_rows fields.","exampleAgentPrompt":"Can you audit the quality, normalise the strings, and pivot by date and category for these pipeline duration records — I have run_seconds and processed_rows across dataset_group 'etl-prod' for about 50 rows from last month? Run the analysis on the run_seconds field.","exampleUseCases":[{"title":"ETL pipeline health check","prompt":"I have 120 rows of daily ETL run logs with run_seconds and processed_rows grouped by dataset_group. Can you run a record quality audit, normalise the string fields, and give me a date-by-category pivot so I can spot any bad data or patterns? Use run_seconds as the target field."},{"title":"Data quality gate before reporting","prompt":"Before I publish our weekly pipeline performance report, can you audit these 80 pipeline duration records for quality issues, clean up the string fields, and pivot run_seconds by date and dataset_group to make sure everything looks consistent?"},{"title":"Debugging slow pipeline categories","prompt":"I've got 150 pipeline run records with run_seconds and processed_rows across several dataset groups — can you do a quality audit, string normalisation, and a date-by-category pivot on processed_rows so I can see which groups are underperforming over time?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values; (2) a string normalisation report cleaning dataset_group and row_id fields; and (3) a date-by-category pivot summarising the selected numeric field (run_seconds or processed_rows) grouped by date and dataset_group — all computed locally with no external lookups.","failureModes":["Input array is empty or exceeds 200 rows — rejected with validation error","Required fields (row_id, dataset_group, date, run_seconds, processed_rows) missing from row objects","Date field does not match YYYY-MM-DD pattern — row fails schema validation","field enum value not one of 'run_seconds' or 'processed_rows' — rejected","Numeric fields exceed allowed magnitude bounds — row rejected","Payment of 0.01 USDC not confirmed — request blocked"],"whenToPreferThis":"Choose this endpoint when you have pipeline duration data (run_seconds, processed_rows) organised by dataset_group and date and need three bundled analytical outputs — quality auditing, string normalisation, and a categorical date pivot — in a single cheap call. It is ideal for data engineering workflows where you want a quick offline audit of up to 200 rows without any external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:17.446Z","isFirstParty":false,"canonicalSlug":"pipeline-durations-analytics-record-quality-audit-string-68695640"}