{"uid":"cap_DAvvyHSqAv24CFoYR8Gug","slug":"study-recruitment-data-quality-audit-string-normalisation-numeric-row-1b880afa","name":"Study Recruitment Data Quality Audit, String Normalisation & Numeric Row Ordering","description":"Study recruitment — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied study recruitment records (eligible_candidates, enrolled_candidates). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-education-09-196?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","course_group","date","eligible_candidates","enrolled_candidates"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"course_group":{"type":"string","default":"","maxLength":80},"eligible_candidates":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"enrolled_candidates":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","course_group":"group-a","eligible_candidates":10,"enrolled_candidates":1},{"date":"2026-10-02","row_id":"row-2","course_group":"group-b","eligible_candidates":20,"enrolled_candidates":2},{"date":"2026-10-03","row_id":"row-3","course_group":"group-a","eligible_candidates":15,"enrolled_candidates":3},{"date":"2026-10-04","row_id":"row-4","course_group":"group-b","eligible_candidates":30,"enrolled_candidates":4}],"maxItems":200,"minItems":1},"field":{"enum":["eligible_candidates","enrolled_candidates"],"type":"string","default":"eligible_candidates"}}},"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_sd21LxV_-aqs-rp2dqQ7E","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":"Runs three local analytical reports on study recruitment records: a record quality audit, string normalisation, and numeric row ordering — all on supplied data, no external calls.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and numeric row ordering on my study recruitment data? I have rows with fields like row_id, course_group, date, eligible_candidates, and enrolled_candidates — up to 50 rows — and I want the analysis focused on eligible_candidates.","exampleUseCases":[{"title":"Pre-analysis data hygiene check","prompt":"I have a batch of study recruitment records with course groups, dates, and candidate counts — can you audit the data quality, normalise the course group strings, and sort the rows by enrolled_candidates so I can spot issues before running my analysis?"},{"title":"Cleaning messy enrollment exports","prompt":"My recruitment export has inconsistent course group labels and possibly missing or out-of-order candidate numbers. Can you run a quality audit, fix the string formatting, and reorder the rows numerically by eligible_candidates for me?"},{"title":"Verifying recruitment data before reporting","prompt":"Before I submit my quarterly recruitment report, can you check these candidate records for quality issues, normalise the text fields, and order everything by enrolled_candidates so the data is clean and consistent?"}],"resultDescription":"Three independent analytical reports derived purely from the supplied rows: (1) a record quality audit flagging missing, null, or malformed values; (2) a string normalisation report cleaning and standardising the course_group and other text fields; (3) a numeric row ordering report sorting rows by the selected numeric field (eligible_candidates or enrolled_candidates). Up to 200 rows supported per call. No external lookups or predictions are made.","failureModes":["More than 200 rows submitted — exceeds maximum batch size","Missing required fields (row_id, date, course_group, eligible_candidates, enrolled_candidates) causing schema validation failure","Invalid date format (must match YYYY-MM-DD pattern)","Invalid field enum value — must be 'eligible_candidates' or 'enrolled_candidates'","Empty rows array submitted (minItems: 1 required)","Numeric values outside allowed range (±1,000,000,000,000)"],"whenToPreferThis":"Choose this endpoint when you need a combined, cost-efficient bundle of three data quality operations — audit, normalisation, and ordering — on study recruitment data in a single call for $0.01 USDC. It is best suited for offline/batch data hygiene workflows on supplied datasets (up to 200 rows) where no live data lookup, external enrichment, prediction, or professional assessment is required. Prefer alternatives if you need real-time candidate screening, predictive enrollment modelling, or data from sources other than study recruitment (e.g. savings ledger or wallet inventory, which have their own sibling endpoints).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:50:47.232Z","isFirstParty":false,"canonicalSlug":"study-recruitment-data-quality-audit-string-normalisation-numeric-row-1b880afa"}