{"uid":"cap_bn_oLZFCZzRXlJ5I1kRJi","slug":"rig-complexity-adjacent-row-changes-explicit-missing-value-fill-143a3685","name":"Rig Complexity Adjacent Row Changes + Explicit Missing-Value Fill","description":"Rig complexity — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied rig complexity records (bone_count, constraint_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-creative-04-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","bone_count","constraint_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"bone_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"constraint_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","bone_count":10,"asset_group":"group-a","constraint_count":1},{"date":"2026-10-02","row_id":"row-2","bone_count":20,"asset_group":"group-b","constraint_count":2},{"date":"2026-10-03","row_id":"row-3","bone_count":15,"asset_group":"group-a","constraint_count":3},{"date":"2026-10-04","row_id":"row-4","bone_count":30,"asset_group":"group-b","constraint_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["bone_count","constraint_count"],"type":"string","default":"bone_count"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_71CdFTtaGQ_N6u_BCaqKf","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":"Analyzes rig complexity records (bone_count, constraint_count) to compute adjacent row changes and fill missing values using zero, mean, or median methods — returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a set of rig complexity records for a few asset groups — can you compute the adjacent row changes for bone_count and also fill in any missing bone_count values using median? Here are my rows: row-1 group-a 2026-10-01 bone_count=10 constraint_count=1, row-2 group-b 2026-10-02 bone_count=null constraint_count=2, row-3 group-a 2026-10-03 bone_count=15 constraint_count=3.","exampleUseCases":[{"title":"Detecting rig bloat over time","prompt":"I have daily rig complexity logs for two asset groups over the past week — can you compute the adjacent row changes in bone_count so I can see where the rig count jumped the most? Use these rows: row-1 group-a 2026-10-01 bone_count=100 constraint_count=5, row-2 group-a 2026-10-02 bone_count=130 constraint_count=6, row-3 group-a 2026-10-03 bone_count=125 constraint_count=5."},{"title":"Cleaning sparse rig complexity exports","prompt":"My rig export has some null bone_count values that I need filled before analysis — can you fill missing bone_count entries using mean and also give me the adjacent row change report? Rows are: row-1 group-x 2026-09-01 bone_count=50 constraint_count=3, row-2 group-x 2026-09-02 bone_count=null constraint_count=4, row-3 group-x 2026-09-03 bone_count=60 constraint_count=4."},{"title":"Constraint count drift analysis","prompt":"Can you analyze my constraint_count records for adjacent row changes and fill any missing constraint_count values with zero? Here's the data: row-1 group-b 2026-11-01 bone_count=20 constraint_count=8, row-2 group-b 2026-11-02 bone_count=22 constraint_count=null, row-3 group-b 2026-11-03 bone_count=19 constraint_count=10."}],"resultDescription":"Returns two analytical reports: (1) an adjacent row changes report showing delta values between consecutive rows for the selected field, and (2) an explicit missing-value fill report showing which null values were replaced and what value was used (zero, mean, or median). Both reports cover the supplied rows (up to 200) and are computed locally without external lookups.","failureModes":["More than 200 rows submitted — request rejected with maxItems violation","Missing required fields (row_id, date, bone_count, constraint_count) — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema rejection","Invalid method enum value outside zero/mean/median — validation error","Invalid field enum value outside bone_count/constraint_count — validation error","Empty rows array — minItems violation"],"whenToPreferThis":"Choose this endpoint when you have structured rig complexity tabular data (bone_count, constraint_count) and need both sequential change detection between adjacent rows and missing-value imputation in a single call. It is ideal for 3D animation pipeline quality checks, rig audit workflows, or cleaning exported rig logs before further analysis. It does not perform live lookups, external enrichment, or predictions — use it purely for local analytical computation on supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-31T12:41:28.359Z","isFirstParty":false,"canonicalSlug":"rig-complexity-adjacent-row-changes-explicit-missing-value-fill-143a3685"}