{"uid":"cap_44sReLQFB8_llBRXM2hAw","slug":"company-headcount-observations-string-normalisation-iqr-outlier-flags-02d22c6c","name":"Company Headcount Observations — String Normalisation + IQR Outlier Flags","description":"Company headcount observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied company headcount observations records (headcount, open_roles). 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-company-12-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","headcount","open_roles"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"headcount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"open_roles":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","headcount":10,"open_roles":1,"company_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","headcount":20,"open_roles":2,"company_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","headcount":15,"open_roles":3,"company_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","headcount":30,"open_roles":4,"company_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["headcount","open_roles"],"type":"string","default":"headcount"}}},"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_ede6ghqOf-lp4ZSCvNI8a","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 company headcount and open-roles observation data for up to 200 rows, producing two analytical reports.","exampleAgentPrompt":"I have a batch of company headcount records — up to 150 rows with fields like headcount and open_roles across different company groups — can you run string normalisation and IQR outlier flagging on the headcount field and give me two reports back?","exampleUseCases":[{"title":"Flagging anomalous headcount spikes","prompt":"I've got 80 rows of monthly headcount observations across three company groups and I suspect some entries are statistical outliers — can you run IQR outlier detection on the headcount field and normalise the company group strings at the same time?"},{"title":"Cleaning open roles data before reporting","prompt":"Before I publish our quarterly workforce report, can you normalise the string fields and flag any IQR outliers in the open_roles column across these 120 company headcount rows grouped by division?"},{"title":"HR data quality audit across subsidiaries","prompt":"We've consolidated headcount data from five subsidiaries into about 200 rows — can you check the headcount field for IQR-based outliers and clean up any inconsistent string formatting in the company_group column so our records are consistent?"}],"resultDescription":"Two independent analytical reports covering: (1) string normalisation results for the company_group field across the submitted rows, and (2) IQR outlier flags for the selected numeric field (headcount or open_roles), identifying which row IDs fall outside the interquartile range bounds. Up to 200 rows processed per call.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, company_group, date, headcount, open_roles) cause row-level errors","Invalid date format (not YYYY-MM-DD) causes schema validation failure","All numeric values null prevents IQR calculation","Single-row input may produce degenerate IQR results with no meaningful outlier detection"],"whenToPreferThis":"Choose this endpoint when you have tabular company headcount or open-roles observations and need both string normalisation of categorical labels and statistical outlier detection (IQR method) in a single batch call. Ideal for HR analytics pipelines, workforce data quality audits, or pre-processing steps before dashboarding or reporting. Best when you have up to 200 rows and want two structured reports without building your own analytics logic.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:19.251Z","isFirstParty":false,"canonicalSlug":"company-headcount-observations-string-normalisation-iqr-outlier-flags-02d22c6c"}