{"uid":"cap_ee-xCpLOiLMqhGEEAyvYJ","slug":"company-headcount-observations-string-normalisation-two-measure-97dfb133","name":"Company Headcount Observations — String Normalisation + Two-Measure Correlation","description":"Company headcount observations — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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-12-033?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}}},"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_kwkRcyEo5cISqQrfUUhai","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 company group strings and computes a two-measure Pearson/rank correlation between headcount and open_roles across up to 200 supplied company observation rows.","exampleAgentPrompt":"I have a list of up to 200 company headcount observations with company group names, dates, headcount numbers, and open roles — can you normalise the company group strings and tell me the correlation between headcount and open_roles across all those rows?","exampleUseCases":[{"title":"HR data quality audit before reporting","prompt":"I've got 150 rows of company workforce data with messy company group labels like 'Group A', 'group-a', 'GROUP A' — can you normalise those strings and also compute the correlation between headcount and open roles so I can include both in my quarterly HR report?"},{"title":"Recruiting pipeline signal analysis","prompt":"We track headcount and open roles daily across several business units — here are 80 observations, can you check whether open roles and headcount are correlated and also clean up the company group names?"},{"title":"Workforce benchmark data preparation","prompt":"Before I merge this dataset with our benchmarking tool, I need to standardise the company_group strings and get a correlation report between the headcount and open_roles columns — I have about 60 rows to process."}],"resultDescription":"Returns two analytical reports derived from the supplied rows: (1) a string normalisation report for the company_group field identifying and standardising variant forms, and (2) a two-measure correlation report quantifying the statistical relationship between headcount and open_roles across all submitted observations. No external data is fetched; analysis is performed entirely on the supplied rows.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — request rejected","date field does not match YYYY-MM-DD pattern — validation error","row_id missing or empty — validation error","Both headcount and open_roles are null for all rows — correlation cannot be computed","Malformed JSON body — parse error"],"whenToPreferThis":"Choose this endpoint when you have tabular company headcount data in hand (up to 200 rows) and need both string normalisation on company group labels and a statistical correlation between headcount and open_roles in a single low-cost call. It is best suited for offline/batch data preparation workflows where no live lookup, prediction, or external enrichment is needed. Prefer it over general-purpose analytics tools when you want a deterministic, reproducible, cheap analytical pass on supplied data without sending it to a large external model.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:45:01.285Z","isFirstParty":false,"canonicalSlug":"company-headcount-observations-string-normalisation-two-measure-97dfb133"}