{"uid":"cap_PP23VW3pkjddlD_EaEXCY","slug":"content-length-observations-record-quality-audit-string-normalisation-8902fdd9","name":"Content Length Observations — Record Quality Audit + String Normalisation + Daily Sum Series","description":"Content length observations — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series for supplied content length observations records (word_count, heading_count). 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-web-19-198?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","site_group","date","word_count","heading_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"site_group":{"type":"string","default":"","maxLength":80},"word_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"heading_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","site_group":"group-a","word_count":10,"heading_count":1},{"date":"2026-10-02","row_id":"row-2","site_group":"group-b","word_count":20,"heading_count":2},{"date":"2026-10-03","row_id":"row-3","site_group":"group-a","word_count":15,"heading_count":3},{"date":"2026-10-04","row_id":"row-4","site_group":"group-b","word_count":30,"heading_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["word_count","heading_count"],"type":"string","default":"word_count"}}},"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_BOQgbynuJ2xxh7WexL86i","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 (record quality audit, string normalisation, and daily sum series) on up to 200 rows of content-length observation data (word_count, heading_count).","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and daily sum series on my content length observations? I have rows with word_count and heading_count fields, grouped by site, and I want the daily sum computed for word_count — here are my rows: [{\"row_id\":\"r1\",\"site_group\":\"blog\",\"date\":\"2024-03-01\",\"word_count\":450,\"heading_count\":5},{\"row_id\":\"r2\",\"site_group\":\"blog\",\"date\":\"2024-03-02\",\"word_count\":320,\"heading_count\":3}].","exampleUseCases":[{"title":"Content quality audit for editorial team","prompt":"I've got 150 rows of article metrics with word_count and heading_count per site group and date — can you run the record quality audit, string normalisation, and daily sum series on them for word_count so I can spot any bad records before we report?"},{"title":"Daily heading count aggregation for SEO dashboard","prompt":"Please run the three analytical reports — quality audit, string normalisation, and daily sum series — on my heading_count observations across these site groups and dates; I want to see how heading counts sum up per day."},{"title":"Data cleaning before content performance analysis","prompt":"Before I hand this content length dataset off to the analytics team, can you audit the record quality, normalise the string fields, and compute a daily sum series for word_count across all 80 rows I'm supplying?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging anomalies or inconsistencies in the supplied rows, (2) a string normalisation report cleaning and standardising string fields, and (3) a daily sum series aggregating the selected numeric field (word_count or heading_count) by date — all computed locally without external lookups.","failureModes":["Submitting more than 200 rows results in rejection or truncation","Missing required fields (row_id, site_group, date, word_count, heading_count) causes validation failure","Malformed date strings not matching YYYY-MM-DD pattern will be rejected","Null numeric values may be excluded from daily sum calculations","Selecting an invalid field value outside the enum [word_count, heading_count] causes an error"],"whenToPreferThis":"Choose this endpoint when you need a bundled, low-cost ($0.01 USDC) triple-report analysis — quality audit, string normalisation, and daily sum aggregation — on up to 200 rows of content-length observation data without requiring any live data lookups or external integrations. It is ideal for pre-processing content metrics datasets before downstream reporting or dashboard ingestion.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:08:51.349Z","isFirstParty":false,"canonicalSlug":"content-length-observations-record-quality-audit-string-normalisation-8902fdd9"}