{"uid":"cap_dEgkQTt5LDHJu8E2VzWYt","slug":"data-reconciliation-base-100-series-explicit-missing-value-fill-3eb7e657","name":"Data Reconciliation — Base-100 Series + Explicit Missing-Value Fill","description":"Data reconciliation — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). 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-11-169?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_total","target_total"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"target_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_total":10,"target_total":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_total":20,"target_total":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_total":15,"target_total":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_total":30,"target_total":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["source_total","target_total"],"type":"string","default":"source_total"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_OqAr_t7Ekc6KN89q7nR7P","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":"Reconciles source and target totals across up to 200 rows by computing a Base-100 indexed series and filling missing values using a chosen strategy (zero, mean, or median).","exampleAgentPrompt":"Can you reconcile my source vs target sales data for October 2026 across groups A and B — 4 rows total — index everything to the first row as the base-100 starting point, and fill any missing source_total values using median imputation?","exampleUseCases":[{"title":"Monthly finance close reconciliation","prompt":"I have 150 rows of monthly ledger data with source_total and target_total figures for several cost centers. Can you run a reconciliation report indexed to row 0 as the base-100 reference and fill any null source_total entries using the mean?"},{"title":"ETL pipeline validation audit","prompt":"After our ETL run, I need to compare source and target row totals for our pipeline output — about 80 rows across two dataset groups. Index it to the first row and use zero-fill for any missing target_total values so I can see where the gaps are."},{"title":"Multi-group sales data normalization","prompt":"We have sales data across three regional groups from Q3, around 60 rows, with some missing source_total entries. Can you normalize everything to a base-100 series starting from row 5 and fill the blanks with the median value?"}],"resultDescription":"Returns two analytical reports per call: a Base-100 indexed series showing how each row's values relate to the chosen base row, and a missing-value filled series where nulls in the selected field are replaced using the chosen method (zero, mean, or median). Both reports cover all submitted rows (up to 200) and operate entirely on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit","base_index references a row index out of range for the submitted dataset","Invalid date format (must be YYYY-MM-DD) causes row validation failure","source_total or target_total values outside ±1,000,000,000,000 are rejected","Empty rows array returns validation error","Missing required fields (row_id, dataset_group, date) cause schema rejection"],"whenToPreferThis":"Choose this endpoint when you need to both normalize numeric reconciliation data to a Base-100 indexed series AND handle missing values in a single API call, without relying on external data sources or predictions. It is ideal for ETL validation, financial close reconciliation, and audit workflows where you have up to 200 rows of source/target pairs and need two distinct analytical outputs cheaply ($0.01 USDC). Prefer alternatives if you need outlier detection, deduplication, or more than 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:28.135Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-base-100-series-explicit-missing-value-fill-3eb7e657"}