{"uid":"cap_MhIpXheXoXo9x6TgPCrue","slug":"air-quality-observations-string-normalisation-b47e4516","name":"Air Quality Observations String Normalisation","description":"Air quality observations - String normalisation: String normalisation for Air quality observations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded string normalisation report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-air-quality-observations-normalise?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station","date","pm25","humidity_percent"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"pm25":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"station":{"type":"string","default":"","maxLength":80},"humidity_percent":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","pm25":10,"row_id":"row-1","station":"group-a","humidity_percent":1},{"date":"2026-10-02","pm25":20,"row_id":"row-2","station":"group-b","humidity_percent":2},{"date":"2026-10-03","pm25":15,"row_id":"row-3","station":"group-a","humidity_percent":3},{"date":"2026-10-04","pm25":30,"row_id":"row-4","station":"group-b","humidity_percent":4}],"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__qBVcdhbgWc2NyD9ruwvE","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 string fields in a batch of air quality observation records (station names, dates) and returns a descriptive string normalisation report.","exampleAgentPrompt":"I have up to 200 air quality observation records with station names, dates, PM2.5 values, and humidity percentages — can you run a string normalisation report on them to clean up and standardise the station and date fields?","exampleUseCases":[{"title":"Station name deduplication before analysis","prompt":"I've got readings from 50 monitoring stations but the station name field is inconsistent — some say 'Station A', others 'station-a' or 'STATION A'. Can you run a string normalisation pass on these air quality records so I can group them properly before doing my analysis?"},{"title":"Pre-pipeline data quality check","prompt":"Before I load these 150 air quality observation rows into our database, can you normalise the string fields — especially station and date — and give me a report flagging anything that looks malformed or inconsistent?"},{"title":"Automated cleaning for daily ingestion","prompt":"Every day I receive a CSV of air quality readings with row IDs, station names, dates in YYYY-MM-DD format, PM2.5 values, and humidity percentages. Can you normalise the string fields and tell me if anything looks off so I can catch data quality issues early?"}],"resultDescription":"A bounded string normalisation report covering the submitted air quality observation records (up to 200 rows). The report includes descriptive analytics on string fields such as station names and dates — covering whitespace issues, case inconsistencies, format conformance, and field-level normalisation summaries. No enrichment, forecasting, or external data is added.","failureModes":["Fewer than 1 row or more than 200 rows submitted — schema validation error","row_id missing or empty — required field violation","date field does not match YYYY-MM-DD pattern — pattern validation failure","pm25 or humidity_percent values outside numeric bounds — range error","Additional properties included in row objects — rejected due to additionalProperties:false","Payment not received or USDC balance insufficient — 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need to clean and standardise string fields (station names, date formats) in a batch of up to 200 air quality observations before downstream analysis, storage, or reporting. It is ideal for pre-pipeline data quality checks and local descriptive analytics without requiring external enrichment or forecasting. Prefer it over general-purpose data cleaning tools when your data is specifically structured air quality observations with PM2.5 and humidity fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T06:50:57.892Z","isFirstParty":false,"canonicalSlug":"air-quality-observations-string-normalisation-b47e4516"}