{"uid":"cap_JCh9y-AVRcfGIWAP2BGPy","slug":"table-extraction-string-normalisation-trailing-mean-series-4d1196a7","name":"Table Extraction — String Normalisation + Trailing Mean Series","description":"Table extraction — String normalisation + Trailing mean series: String normalisation + Trailing mean series for supplied table extraction records (extracted_rows, expected_rows). 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-web-10-028?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","site_group","date","extracted_rows","expected_rows"],"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},"expected_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"extracted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","site_group":"group-a","expected_rows":1,"extracted_rows":10},{"date":"2026-10-02","row_id":"row-2","site_group":"group-b","expected_rows":2,"extracted_rows":20},{"date":"2026-10-03","row_id":"row-3","site_group":"group-a","expected_rows":3,"extracted_rows":15},{"date":"2026-10-04","row_id":"row-4","site_group":"group-b","expected_rows":4,"extracted_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["extracted_rows","expected_rows"],"type":"string","default":"extracted_rows"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_wux9azwFPLbsfXYIxZXAb","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":"Analyzes table extraction records by applying string normalization and computing trailing mean series across up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you run string normalization and a trailing mean series analysis on my table extraction data? I have 4 rows for site groups 'group-a' and 'group-b' from October 2026 — use the extracted_rows field with a window of 2.","exampleUseCases":[{"title":"Web scraper accuracy trend analysis","prompt":"I have daily table extraction logs from two site groups over the past month — can you normalize the string fields and compute a trailing mean with window 3 on extracted_rows so I can see the smoothed extraction trend?"},{"title":"Expected vs extracted row discrepancy review","prompt":"Run a string normalization and trailing mean series report on my extraction QA data — I want to analyze expected_rows with a rolling window of 5 across 50 rows tagged by site group and date."},{"title":"ETL pipeline extraction monitoring","prompt":"I need to audit my ETL pipeline's table extraction output — can you apply string normalization and compute a trailing mean (window size 2) on the extracted_rows field for these 20 records by date and site group?"}],"resultDescription":"Two analytical reports for up to 200 input rows: (1) a string normalization report standardizing categorical/text fields in the extraction records, and (2) a trailing mean series computed over the specified numeric field (extracted_rows or expected_rows) using the given rolling window — all derived locally from the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Invalid date format (must match YYYY-MM-DD pattern)","Missing required fields (row_id, site_group, date, extracted_rows, expected_rows)","Window size out of range (must be 2–200)","All values null in selected field — trailing mean cannot be computed","Malformed JSON body returns validation error"],"whenToPreferThis":"Choose this endpoint when you need both string normalization and rolling/trailing mean analysis on table extraction records in a single low-cost call ($0.01 USDC for both reports). Ideal for web scraping QA workflows, ETL auditing, or extraction accuracy monitoring where you have up to 200 date-stamped rows with extracted and expected row counts across site groups. Prefer this over general analytics tools when you want two bundled reports computed locally from your own supplied data without any external data fetching or live prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:47:29.807Z","isFirstParty":false,"canonicalSlug":"table-extraction-string-normalisation-trailing-mean-series-4d1196a7"}