{"uid":"cap_gmpeK4sjLJM5Q23KvzVxs","slug":"company-headcount-observations-string-normalisation-numeric-a5fe2b93","name":"Company Headcount Observations — String Normalisation + Numeric Distribution","description":"Company headcount observations — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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-12-022?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_gO4gKb3mRGMsKH2CTHnJZ","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":"Analyzes up to 200 company headcount observation records by producing string normalization and numeric distribution reports for headcount and open_roles fields.","exampleAgentPrompt":"Can you run string normalization and numeric distribution analysis on these 50 company headcount records — I have row IDs, company groups, dates, headcount numbers, and open roles counts — and focus the distribution report on the headcount field?","exampleUseCases":[{"title":"HR data quality audit across business units","prompt":"I have 120 headcount observation records from our HR system spanning multiple business units and dates. Can you normalize the company group strings and give me a numeric distribution on the headcount field so I can spot anomalies?"},{"title":"Recruitment pipeline open-roles analysis","prompt":"We've been tracking open roles at 80 companies over the past quarter. Can you run a numeric distribution report on open_roles across our observation records so I can understand the spread and typical values?"},{"title":"Multi-company workforce benchmarking","prompt":"I've got headcount data for about 150 company-date observations across several groups. Can you normalize the company name strings and compute the numeric distribution for headcount so I can compare workforce sizes?"}],"resultDescription":"Returns two analytical reports: (1) a string normalization report covering company group labels and other string fields in the headcount observation records, and (2) a numeric distribution report (min, max, mean, median, percentiles, etc.) for the specified numeric field — either headcount or open_roles — across up to 200 supplied rows.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, company_group, date, headcount, open_roles) causes rejection","Invalid date format (non-YYYY-MM-DD) triggers a pattern validation error","Supplying an invalid field enum value (not 'headcount' or 'open_roles') returns an error","Empty rows array is rejected due to minItems:1 constraint","Numbers outside the allowed range (±1 trillion) are rejected"],"whenToPreferThis":"Choose this endpoint when you have raw company headcount observation data (up to 200 rows) and need both string normalization and numeric distribution analysis in a single call. It is well-suited for HR analytics, workforce benchmarking, and data quality workflows where you want statistical summaries and cleaned string labels without building custom analytics. Prefer it over general-purpose analytics tools when you need a fast, low-cost ($0.01 USDC) bundled report on headcount or open_roles data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:22.183Z","isFirstParty":false,"canonicalSlug":"company-headcount-observations-string-normalisation-numeric-a5fe2b93"}