{"uid":"cap__RJPf9-LLmPoDhXKYx5lj","slug":"camera-coverage-string-normalisation-and-iqr-outlier-flags-72ccf83b","name":"Camera Coverage String Normalisation and IQR Outlier Flags","description":"Camera coverage — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied camera coverage records (shot_count, coverage_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-12-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","shot_count","coverage_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"shot_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"coverage_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","shot_count":10,"asset_group":"group-a","coverage_seconds":1},{"date":"2026-10-02","row_id":"row-2","shot_count":20,"asset_group":"group-b","coverage_seconds":2},{"date":"2026-10-03","row_id":"row-3","shot_count":15,"asset_group":"group-a","coverage_seconds":3},{"date":"2026-10-04","row_id":"row-4","shot_count":30,"asset_group":"group-b","coverage_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["shot_count","coverage_seconds"],"type":"string","default":"shot_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_e4ghsYjB_101OWoQbkw57","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 flags IQR-based outliers in camera coverage records (shot_count, coverage_seconds) for up to 200 rows with 2 independent analytical reports.","exampleAgentPrompt":"Can you normalise the string fields and flag any IQR outliers in my camera coverage records — here are 4 rows with shot_count and coverage_seconds across group-a and group-b from 2026-10-01 to 2026-10-04 — I want to analyse shot_count specifically?","exampleUseCases":[{"title":"Outlier detection in daily shot counts","prompt":"I have 50 rows of daily camera coverage data with shot_count and coverage_seconds across several asset groups — can you run string normalisation and IQR outlier flagging on the shot_count field so I can spot any anomalous shooting days?"},{"title":"Coverage seconds anomaly check for post-production","prompt":"We're reviewing camera footage allocation and I need to check our coverage_seconds records for statistical outliers — can you normalise the strings and flag IQR anomalies across these 120 rows of camera asset data?"},{"title":"QA audit of multi-group camera records","prompt":"Before we archive these camera records, can you run IQR outlier detection and string normalisation on both shot_count and coverage_seconds across our 80-row dataset spanning group-a and group-b assets from last quarter?"}],"resultDescription":"Two independent analytical reports per call: one covering string normalisation of the supplied camera coverage fields (asset_group, date, row_id), and one flagging IQR-based outliers in the selected numeric field (shot_count or coverage_seconds), applied across up to 200 submitted rows. No external lookups or predictions are made — all analysis is performed locally on the supplied data.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, asset_group, date, shot_count, coverage_seconds) causes rejection","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Invalid field enum value (anything other than shot_count or coverage_seconds) causes failure"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline statistical quality checks on camera coverage tabular data — specifically IQR-based outlier detection combined with string normalisation — without any external API calls, predictions, or professional assessments. It is well-suited for pre-archive QA, production data audits, or pipeline data cleaning steps where up to 200 rows of camera shot and coverage records need to be validated cheaply ($0.01 USDC for 2 reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:40:38.006Z","isFirstParty":false,"canonicalSlug":"camera-coverage-string-normalisation-and-iqr-outlier-flags-72ccf83b"}