{"uid":"cap_Js1bdQS-K_vpi5gpKiDhO","slug":"company-headcount-observations-string-normalisation-date-by-category-ac9ae795","name":"Company Headcount Observations — String Normalisation + Date by Category Pivot","description":"Company headcount observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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-12-035?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__56PHi3qx01psuqQbyV_o","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 name strings and pivots headcount/open_roles observations by date and category group, producing two analytical reports from supplied row data.","exampleAgentPrompt":"I have up to 200 rows of company headcount observations — each with a row_id, company_group, date, headcount, and open_roles — can you normalize the company group strings and produce a date-by-category pivot for the headcount field?","exampleUseCases":[{"title":"Quarterly workforce snapshot pivot","prompt":"Here are 150 rows of headcount observations across our divisions from Q3, each with a company_group, date, headcount, and open_roles — please normalize the group names and pivot the headcount figures by date and category so I can see the breakdown clearly."},{"title":"Open roles trend by business unit","prompt":"I've got a dataset of open_roles counts across multiple business units with dates spanning six months — can you normalize the unit name strings and create a date-by-category pivot using the open_roles field?"},{"title":"Cleaning messy company labels in HR data","prompt":"Our HR export has 80 rows where the company_group column has inconsistent capitalisation and spacing — run string normalization on those and also pivot the headcount numbers by date and company group for me."}],"resultDescription":"Two analytical reports: (1) a string-normalised version of the company_group labels across all supplied rows, and (2) a pivot table of the selected field (headcount or open_roles) indexed by date and company_group category — all computed locally from the supplied data with no external lookups.","failureModes":["Rows exceeding 200 items will be rejected — trim input to 200 rows maximum","Missing required fields (row_id, company_group, date, headcount, open_roles) cause validation errors","Dates not matching YYYY-MM-DD pattern are rejected","Null values in headcount or open_roles are accepted but may produce gaps in pivot output","Invalid field enum value (anything other than 'headcount' or 'open_roles') causes a schema error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of company headcount observation records that need both company group string normalization and a date-by-category pivot in a single low-cost call. It is ideal for HR analytics workflows where raw export data has inconsistent company labels and you need a structured time-series pivot without any external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:45:33.838Z","isFirstParty":false,"canonicalSlug":"company-headcount-observations-string-normalisation-date-by-category-ac9ae795"}