{"uid":"cap_FacyHQNLfk5yThGeYm5RQ","slug":"damaged-stock-string-normalisation-and-base-100-series-7c911043","name":"Damaged Stock String Normalisation and Base-100 Series","description":"Damaged stock — String normalisation + Base-100 series: String normalisation + Base-100 series for supplied damaged stock records (handled_units, damaged_units). 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-retail-18-030?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","handled_units","damaged_units"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"store_group":{"type":"string","default":"","maxLength":80},"damaged_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"handled_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","store_group":"group-a","damaged_units":1,"handled_units":10},{"date":"2026-10-02","row_id":"row-2","store_group":"group-b","damaged_units":2,"handled_units":20},{"date":"2026-10-03","row_id":"row-3","store_group":"group-a","damaged_units":3,"handled_units":15},{"date":"2026-10-04","row_id":"row-4","store_group":"group-b","damaged_units":4,"handled_units":30}],"maxItems":200,"minItems":1},"field":{"enum":["handled_units","damaged_units"],"type":"string","default":"handled_units"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_6TvSDVAy61pAsaM7nGZJx","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 store-group strings and computes a Base-100 index series for up to 200 rows of damaged stock data (handled_units, damaged_units), returning two local analytical reports.","exampleAgentPrompt":"I have damaged stock records for two store groups over four days — handled_units and damaged_units per row — can you normalise the store group strings and compute a Base-100 index series for handled_units starting from row index 0? Here are the rows: row-1 group-a 2026-10-01 10 handled 1 damaged, row-2 group-b 2026-10-02 20 handled 2 damaged, row-3 group-a 2026-10-03 15 handled 3 damaged, row-4 group-b 2026-10-04 30 handled 4 damaged.","exampleUseCases":[{"title":"Index weekly damage rates for a retail chain","prompt":"I've got 12 weeks of damaged and handled unit counts across three store groups — can you normalise the store group names and produce a Base-100 index series for damaged_units starting from the very first row so I can see how damage rates have moved relative to that baseline?"},{"title":"Clean store labels before loading to dashboard","prompt":"Our damaged stock export has inconsistent capitalisation and spacing in the store_group field — can you run string normalisation on these 80 rows and also give me a Base-100 series for handled_units anchored at row index 0?"},{"title":"Compare two store groups on indexed handled volume","prompt":"I want to compare how handled unit volumes trend across group-a and group-b over the past month — normalise the store names and compute a Base-100 series for handled_units using row 0 as the base, here are the 30 daily rows."}],"resultDescription":"Returns two local analytical reports: (1) a string normalisation report standardising store_group labels across all submitted rows, and (2) a Base-100 index series for the chosen field (handled_units or damaged_units) relative to the specified base_index row. No external data is fetched; all computation is performed on the supplied rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","base_index references a row position that does not exist in the submitted array","date field does not match YYYY-MM-DD pattern — validation error","handled_units or damaged_units values outside ±1,000,000,000,000 — schema rejection","Missing required fields (row_id, store_group, date, handled_units, damaged_units) on any row"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of store/group labels and a Base-100 indexed time series from the same damaged stock dataset in a single call. It is ideal for retail inventory analysts who need to clean categorical string fields and produce relative index trends without any external API calls, live data lookups, or predictive modelling.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:36:46.431Z","isFirstParty":false,"canonicalSlug":"damaged-stock-string-normalisation-and-base-100-series-7c911043"}