{"uid":"cap_Kr0VjJzEpo763-JH16K7Y","slug":"simulation-caches-adjacent-row-changes-base-100-series-04b72beb","name":"Simulation Caches — Adjacent Row Changes + Base-100 Series","description":"Simulation caches — Adjacent row changes + Base-100 series: Adjacent row changes + Base-100 series for supplied simulation caches records (cache_gb, simulation_seconds). 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-creative-08-155?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","cache_gb","simulation_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"cache_gb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"simulation_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","cache_gb":10,"asset_group":"group-a","simulation_seconds":1},{"date":"2026-10-02","row_id":"row-2","cache_gb":20,"asset_group":"group-b","simulation_seconds":2},{"date":"2026-10-03","row_id":"row-3","cache_gb":15,"asset_group":"group-a","simulation_seconds":3},{"date":"2026-10-04","row_id":"row-4","cache_gb":30,"asset_group":"group-b","simulation_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["cache_gb","simulation_seconds"],"type":"string","default":"cache_gb"},"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_DGLCzxQiTUDl_g-QP8jQW","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":"Computes adjacent row-over-row changes and a base-100 indexed series for user-supplied simulation cache records (cache_gb, simulation_seconds) — two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run the adjacent row changes and base-100 series reports on my simulation cache data? Here are 4 rows — group-a on 2026-10-01 had cache_gb=10 and simulation_seconds=1, group-b on 2026-10-02 had cache_gb=20 and simulation_seconds=2, group-a on 2026-10-03 had cache_gb=15 and simulation_seconds=3, and group-b on 2026-10-04 had cache_gb=30 and simulation_seconds=4. Use cache_gb as the field and row index 0 as the base.","exampleUseCases":[{"title":"Cache growth delta analysis","prompt":"I have daily simulation cache readings across two asset groups for October 2026 — can you compute the adjacent row-over-row changes in cache_gb for these 4 records and also give me a base-100 series starting from row 0? Row data: group-a 2026-10-01 cache_gb=10 sim_seconds=1, group-b 2026-10-02 cache_gb=20 sim_seconds=2, group-a 2026-10-03 cache_gb=15 sim_seconds=3, group-b 2026-10-04 cache_gb=30 sim_seconds=4."},{"title":"Simulation throughput baseline indexing","prompt":"For my simulation throughput data, I want to see how simulation_seconds evolved relative to the first row as a base-100 index — can you run both the adjacent changes report and the base-100 series on these records using simulation_seconds as the field, with base_index set to 0? Records: row-1 group-a 2026-10-01 cache_gb=10 sim_seconds=100, row-2 group-b 2026-10-02 cache_gb=20 sim_seconds=150, row-3 group-a 2026-10-03 cache_gb=15 sim_seconds=130."},{"title":"Mid-series baseline normalization","prompt":"I need a base-100 series for my cache_gb readings starting from row 2 as the baseline, plus the adjacent row differences — here are my 5 simulation records: row-1 group-a 2026-09-01 cache_gb=50 sim_seconds=5, row-2 group-b 2026-09-02 cache_gb=60 sim_seconds=6, row-3 group-a 2026-09-03 cache_gb=80 sim_seconds=8, row-4 group-b 2026-09-04 cache_gb=75 sim_seconds=7, row-5 group-a 2026-09-05 cache_gb=90 sim_seconds=9. Use base_index=2."}],"resultDescription":"Returns two analytical reports for the supplied simulation cache rows: (1) an adjacent row changes report showing the delta of the selected field (cache_gb or simulation_seconds) between each consecutive row, and (2) a base-100 indexed series showing each row's value normalized relative to the row at base_index. Results are computed locally with no external lookup. Up to 200 rows supported per call.","failureModes":["Empty or missing rows array returns an error","Rows exceeding the 200-item limit are rejected","Invalid date format (non YYYY-MM-DD) causes validation failure","base_index value exceeding the number of supplied rows triggers an out-of-bounds error","Null values in the selected numeric field may produce undefined or skipped delta results","Missing required row fields (row_id, asset_group, date) cause schema validation errors"],"whenToPreferThis":"Choose this endpoint when you need two specific analytical transformations — adjacent row-over-row deltas AND a base-100 normalized series — applied to simulation cache records (cache_gb or simulation_seconds) in a single low-cost call. It is ideal for time-series change analysis on small-to-medium datasets (up to 200 rows) where you supply the data directly and need fast, offline computation without live data lookup or prediction. Not suitable for real-time monitoring, external data retrieval, or analytical operations beyond these two report types.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:48:55.246Z","isFirstParty":false,"canonicalSlug":"simulation-caches-adjacent-row-changes-base-100-series-04b72beb"}