{"uid":"cap_iZhPLeyePLUjaZ29F85ZO","slug":"endpoint-telemetry-string-normalisation-and-date-by-category-pivot-739e2142","name":"Endpoint Telemetry String Normalisation and Date-by-Category Pivot","description":"Endpoint telemetry — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied endpoint telemetry records (agent_count, reporting_agents). 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-security-15-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","control_group","date","agent_count","reporting_agents"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"agent_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"control_group":{"type":"string","default":"","maxLength":80},"reporting_agents":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","agent_count":10,"control_group":"group-a","reporting_agents":1},{"date":"2026-10-02","row_id":"row-2","agent_count":20,"control_group":"group-b","reporting_agents":2},{"date":"2026-10-03","row_id":"row-3","agent_count":15,"control_group":"group-a","reporting_agents":3},{"date":"2026-10-04","row_id":"row-4","agent_count":30,"control_group":"group-b","reporting_agents":4}],"maxItems":200,"minItems":1},"field":{"enum":["agent_count","reporting_agents"],"type":"string","default":"agent_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_DdD97FEVo-vPOHFaKsvgG","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":"Normalises string fields and produces a date-by-category pivot table from up to 200 rows of endpoint telemetry data (agent_count, reporting_agents) in two bundled reports.","exampleAgentPrompt":"Take these 4 rows of endpoint telemetry data — row-1 on 2026-10-01 with control_group group-a and agent_count 10 and reporting_agents 1, row-2 on 2026-10-02 with group-b agent_count 20 reporting_agents 2, row-3 on 2026-10-03 group-a agent_count 15 reporting_agents 3, row-4 on 2026-10-04 group-b agent_count 30 reporting_agents 4 — normalise the string fields and give me a date-by-category pivot on agent_count.","exampleUseCases":[{"title":"Pivot agent counts by control group","prompt":"I have 50 rows of endpoint telemetry with dates, control groups, and agent counts. Can you normalise the control_group strings and pivot agent_count by date and category so I can see how each group trends over time?"},{"title":"Clean reporting agent labels before dashboarding","prompt":"Before I load my telemetry into the dashboard, can you normalise all the string fields in these 120 telemetry rows and pivot reporting_agents by date and control_group?"},{"title":"Validate string consistency across telemetry batches","prompt":"I've got two batches of endpoint telemetry totalling about 180 rows. Please normalise the string fields across both and give me the date-by-category pivot on agent_count so I can spot inconsistencies."}],"resultDescription":"Two analytical reports: (1) string-normalised versions of text fields (e.g. control_group) across all submitted rows, and (2) a date-by-category pivot table for the selected numeric field (agent_count or reporting_agents), covering up to 200 input rows. No external lookups or predictions are made.","failureModes":["More than 200 rows submitted — request rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row rejection","Missing required fields (row_id, control_group, date) cause validation errors","Numeric values outside ±1,000,000,000,000 range rejected","Empty rows array returns error"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and a date-by-category pivot in a single call on endpoint telemetry records (agent_count or reporting_agents), particularly when your dataset fits within 200 rows and you want two bundled local analytical reports without any external lookup or live data dependency.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:38:53.299Z","isFirstParty":false,"canonicalSlug":"endpoint-telemetry-string-normalisation-and-date-by-category-pivot-739e2142"}