{"uid":"cap_b6Gqkr9HKIsiX2K9mOcgu","slug":"geospatial-tile-processing-record-quality-audit-string-normalisation-25dcd555","name":"Geospatial Tile Processing — Record Quality Audit + String Normalisation + Keyed Dataset Comparison","description":"Geospatial tile processing — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied geospatial tile processing records (tile_count, processing_seconds). 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-weather-21-207?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","tile_count","processing_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tile_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"processing_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","tile_count":10,"station_group":"group-a","processing_seconds":1},{"date":"2026-10-02","row_id":"row-2","tile_count":20,"station_group":"group-b","processing_seconds":2},{"date":"2026-10-03","row_id":"row-3","tile_count":15,"station_group":"group-a","processing_seconds":3},{"date":"2026-10-04","row_id":"row-4","tile_count":30,"station_group":"group-b","processing_seconds":4}],"maxItems":200,"minItems":1},"other":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","tile_count","processing_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tile_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"processing_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","tile_count":10,"station_group":"group-a","processing_seconds":1},{"date":"2026-10-02","row_id":"row-2","tile_count":21,"station_group":"group-b","processing_seconds":2},{"date":"2026-10-03","row_id":"row-3","tile_count":17,"station_group":"group-a","processing_seconds":3},{"date":"2026-10-04","row_id":"row-4","tile_count":33,"station_group":"group-b","processing_seconds":4}],"maxItems":200,"minItems":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_Z_Z92yqP_AzhjpWH6Y4Xj","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":"Audits record quality, normalises strings, and compares two keyed datasets of geospatial tile processing records (tile_count, processing_seconds) returning three independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and keyed dataset comparison on my two geospatial tile processing datasets? Here's the primary dataset with 4 rows (row-1 through row-4, dates 2026-10-01 to 2026-10-04, station groups group-a and group-b, tile counts 10/20/15/30, processing seconds 1/2/3/4) and a second dataset with the same row IDs but slightly different tile counts (10/21/17/31).","exampleUseCases":[{"title":"Tile pipeline output quality check","prompt":"I have a batch of 50 geospatial tile processing records from last week with station groups, tile counts, and processing seconds. Can you audit the record quality, normalise the station group strings, and flag any issues — the rows are labelled row-1 through row-50 with dates from 2026-09-01 onwards?"},{"title":"Before-and-after dataset comparison","prompt":"We ran a reprocessing job on our tile pipeline and I want to compare the original and reprocessed datasets by row ID to find what changed. Both datasets have the same 100 rows with fields: row_id, station_group, date, tile_count, and processing_seconds. Can you do a keyed comparison and also normalise the station group strings in both?"},{"title":"Station group string normalisation audit","prompt":"Our geospatial tile logs have inconsistent station_group labels across 80 records — things like 'Group-A', 'group_a', 'GROUP A'. Can you normalise those strings, audit overall record quality for tile_count and processing_seconds, and compare against our reference dataset to see which rows differ?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging nulls, out-of-range values, and data completeness issues across the supplied rows; (2) a string normalisation report for text fields such as station_group; and (3) a keyed dataset comparison report detailing differences between the 'rows' and 'other' input datasets matched by row_id. All processing is performed locally with no external lookups.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, station_group, date, tile_count, processing_seconds) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (±1,000,000,000,000) — rejected","Empty rows array (minItems: 1 not met) — validation error","Network or payment failure via x402 protocol — no report returned"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports — record quality audit, string normalisation, and keyed dataset comparison — specifically for geospatial tile processing records with tile_count and processing_seconds fields. It is best suited for offline/batch analysis of up to 200 rows at a low flat cost ($0.01 USDC for all three reports) with no need for live lookups, predictions, or external data enrichment. Prefer alternatives if you need real-time geospatial data, live tile processing APIs, or more than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:50:56.519Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-record-quality-audit-string-normalisation-25dcd555"}