{"uid":"cap_DLG0ZwA0UuD7CE0C0_x10","slug":"company-headcount-observations-string-normalisation-daily-sum-series-9146b091","name":"Company Headcount Observations — String Normalisation + Daily Sum Series","description":"Company headcount observations — String normalisation + Daily sum series: String normalisation + Daily sum series 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-12-027?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_La8pZMR2iUr8ZAAJFCaQm","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 computes daily sum time series for headcount or open_roles observations across up to 200 supplied rows, returning two analytical reports.","exampleAgentPrompt":"Can you normalise the company group names and produce a daily sum series for headcount from these observation rows? I have up to 50 records with fields row_id, company_group, date, headcount, and open_roles.","exampleUseCases":[{"title":"Clean HR data before reporting","prompt":"I have 120 headcount observation rows with messy company group names — can you normalise those strings and give me a daily sum series for headcount so I can plug the clean totals into our weekly HR report?"},{"title":"Daily open roles trend by company","prompt":"Here are 80 hiring pipeline records with company_group, date, and open_roles fields. Can you normalise the company names and compute a daily total open_roles series so I can see how our hiring demand changes day by day?"},{"title":"Validate and aggregate workforce snapshot","prompt":"I've got a dataset of 150 company headcount observations that may have inconsistent employer name formats — please normalise the company_group strings and produce a daily sum series for headcount so I can spot any anomalies across the timeline."}],"resultDescription":"Returns two reports: (1) a string-normalised version of the company_group field across all supplied rows, and (2) a daily sum time series for the selected field (headcount or open_roles), aggregated per date across all company groups in the supplied data. No external data sources are consulted.","failureModes":["Rows exceeding 200 items will be rejected due to maxItems constraint","Missing required fields (row_id, company_group, date, headcount, open_roles) will cause validation errors","Invalid date format (not YYYY-MM-DD) will fail schema pattern validation","Null values in headcount or open_roles may affect sum accuracy in the daily series","Supplying an invalid field enum value (not 'headcount' or 'open_roles') will cause an error"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 company headcount or open_roles observation records that need both string normalisation of company names and daily aggregation into a sum series — delivered together as two reports in a single low-cost call. Prefer this over general-purpose analytics when you specifically need the combination of text normalisation and time-series daily summation on HR workforce data without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:06.954Z","isFirstParty":false,"canonicalSlug":"company-headcount-observations-string-normalisation-daily-sum-series-9146b091"}