{"uid":"cap_7SVREsfIYSTb5R7FXp5JN","slug":"schema-conformity-record-quality-audit-string-normalisation-date-by-edcc3cab","name":"Schema Conformity — Record Quality Audit + String Normalisation + Date by Category Pivot","description":"Schema conformity — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied schema conformity records (valid_records, checked_records). 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-02-206?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","valid_records","checked_records"],"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},"valid_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"checked_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","valid_records":10,"checked_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","valid_records":20,"checked_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","valid_records":15,"checked_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","valid_records":30,"checked_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["valid_records","checked_records"],"type":"string","default":"valid_records"}}},"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_veP9E8aixKM8aVLqZda6b","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 schema conformity data: a record quality audit, string normalisation, and a date-by-category pivot for up to 200 rows of valid/checked record counts.","exampleAgentPrompt":"Can you run the three-part schema conformity analysis — record quality audit, string normalisation, and date-by-category pivot — on my dataset? I have rows with fields like row_id, dataset_group, date, valid_records, and checked_records (up to 200 rows), and I want to focus the pivot on the valid_records field.","exampleUseCases":[{"title":"Data pipeline quality check","prompt":"I have a batch of up to 150 schema conformity records with row IDs, dataset groups, dates, valid_records, and checked_records — can you run a record quality audit, normalise the strings, and give me a date-by-category pivot on valid_records so I can spot issues before the next pipeline run?"},{"title":"Monthly dataset group comparison","prompt":"Run the three schema conformity reports on my 80 rows of monthly data — I need the record quality audit, string normalisation, and a pivot of checked_records by date and dataset group to compare group-a and group-b performance."},{"title":"Pre-release data validation","prompt":"Before I publish this dataset, can you audit the record quality, normalise the string fields, and pivot valid_records by date and category for these 60 schema conformity rows? I want to make sure everything looks clean."}],"resultDescription":"Three independent analytical reports returned together: (1) a record quality audit summarising valid vs. checked record metrics, (2) a string normalisation report cleaning and standardising text fields, and (3) a date-by-category pivot table breaking down the selected numeric field (valid_records or checked_records) across dates and dataset groups. All computed locally with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, date, valid_records, checked_records) on any row — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Numeric fields outside allowed range (±1,000,000,000,000) — rejected","Invalid field enum value (not valid_records or checked_records) — error returned"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, date pivot) on schema conformity data in a single cost-efficient call ($0.01 USDC for all three). Ideal for batch data quality workflows on datasets up to 200 rows where no live external lookup or prediction is needed — all computation is local and deterministic.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:21.238Z","isFirstParty":false,"canonicalSlug":"schema-conformity-record-quality-audit-string-normalisation-date-by-edcc3cab"}