{"uid":"cap_0NXtUqy5fWr0LE6hiJlQB","slug":"request-latency-base-100-series-and-date-by-category-pivot-c966edaf","name":"Request Latency Base-100 Series and Date-by-Category Pivot","description":"Request latency — Base-100 series + Date by category pivot: Base-100 series + Date by category pivot for supplied request latency records (latency_ms, request_count). 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-software-03-167?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","component","date","latency_ms","request_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"component":{"type":"string","default":"","maxLength":80},"latency_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"request_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","component":"group-a","latency_ms":10,"request_count":1},{"date":"2026-10-02","row_id":"row-2","component":"group-b","latency_ms":20,"request_count":2},{"date":"2026-10-03","row_id":"row-3","component":"group-a","latency_ms":15,"request_count":3},{"date":"2026-10-04","row_id":"row-4","component":"group-b","latency_ms":30,"request_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["latency_ms","request_count"],"type":"string","default":"latency_ms"},"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_h6UlDBe69sSLtQ22trZXD","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":"Generates two analytical reports — a Base-100 indexed time series and a date-by-category pivot table — from supplied request latency and request count records.","exampleAgentPrompt":"Can you run a base-100 latency analysis on these records — index from row 0 on the latency_ms field — and also give me a date-by-category pivot table? Here are my rows: component 'auth-service' on 2025-01-01 had latency_ms 120 and request_count 500, and 'payment-service' on 2025-01-01 had latency_ms 200 and request_count 300.","exampleUseCases":[{"title":"Microservice latency comparison over time","prompt":"I have daily latency readings for three microservices over the past month — can you index them all to base 100 starting from day one and also pivot them by date and service name so I can see which component degraded the most? The field I care about is latency_ms."},{"title":"Request volume trend indexing","prompt":"Here are two weeks of request count data across four API components — can you build a base-100 index starting from the first row on the request_count field, and also give me a pivot of request counts by date and component?"},{"title":"SLO investigation after incident","prompt":"We had a latency incident last week — I have per-component daily latency_ms and request_count rows for the two weeks around it. Can you normalize latency to base 100 from the first row and pivot the data by date and component so I can spot which service spiked?"}],"resultDescription":"Two analytical reports: (1) a Base-100 time series where each value is indexed relative to a chosen baseline row, and (2) a date-by-category pivot table aggregating the selected metric (latency_ms or request_count) across dates and component categories — both computed locally from the supplied rows, up to 200 rows total.","failureModes":["Missing required fields (row_id, component, date, latency_ms, request_count) cause validation errors","More than 200 rows submitted exceeds the row limit","Invalid date format (not YYYY-MM-DD) fails pattern validation","base_index pointing to a non-existent row index causes reference errors","Null values in the selected metric field may produce gaps or skipped rows in the series","Payment failure via x402 protocol blocks the request"],"whenToPreferThis":"Choose this endpoint when you have raw request latency or request count records (up to 200 rows) and need both a base-100 normalized time series and a date-by-category pivot table in a single low-cost call. It is ideal for lightweight observability analytics without requiring a full BI tool or data warehouse. Prefer it over manual computation when you need two complementary views (trend indexing + pivot) delivered together from user-supplied data with no external data dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T12:45:46.665Z","isFirstParty":false,"canonicalSlug":"request-latency-base-100-series-and-date-by-category-pivot-c966edaf"}