{"uid":"cap_GQEJewxCbR_Wfhk8thr50","slug":"training-assessments-string-normalisation-iqr-outlier-flags-28b218c6","name":"Training Assessments — String Normalisation + IQR Outlier Flags","description":"Training assessments — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied training assessments records (pre_score, post_score). 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-education-11-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","course_group","date","pre_score","post_score"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"pre_score":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"post_score":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"course_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","pre_score":10,"post_score":1,"course_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","pre_score":20,"post_score":2,"course_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","pre_score":15,"post_score":3,"course_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","pre_score":30,"post_score":4,"course_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["pre_score","post_score"],"type":"string","default":"pre_score"}}},"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_wX_fbrbX2-9PlBLsYf3iY","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 and flags IQR-based outliers in pre_score and post_score columns for up to 200 training assessment rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you normalize the string fields and flag any IQR outliers in these training assessment records? I have up to 50 rows with row IDs, course groups, dates, and pre_score values — please run the outlier check on pre_score.","exampleUseCases":[{"title":"Quality check on corporate training scores","prompt":"I have 120 training assessment records from our corporate program with row IDs, course groups, dates, and pre- and post-scores — can you normalize the string fields and flag any IQR outliers in the post_score column so I can spot unusual results before reporting?"},{"title":"Detecting anomalous pre-scores in a course cohort","prompt":"Here are 80 rows of learner assessment data with row_id, course_group, date, pre_score, and post_score — please run the string normalisation and IQR outlier flagging on pre_score so I can identify which students had unusual baseline results."},{"title":"Data cleaning before training effectiveness analysis","prompt":"Before I analyse training effectiveness, I need to clean my assessment dataset — can you normalise the string fields and flag IQR outliers for the post_score across these 150 training records grouped by course_group?"}],"resultDescription":"Two independent analytical reports per call: one covering string normalisation of fields like course_group and date, and one flagging per-row IQR-based outliers for the selected score field (pre_score or post_score). Each report covers up to 200 input rows and is generated entirely from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, course_group, date, pre_score, post_score) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric score values outside allowed range cause schema rejection","Empty rows array (minItems:1 violated) returns an error","All scores identical — IQR is zero, outlier flagging may return no flags or undefined behaviour"],"whenToPreferThis":"Choose this endpoint when you need a fast, local, privacy-preserving statistical pass over training assessment data — specifically string normalisation of categorical/text fields combined with IQR-based outlier detection on numeric pre/post scores. It is ideal for pre-processing education datasets before downstream analysis, when you want two bundled reports in a single low-cost call and have no need for predictions, external lookups, or professional assessments.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:51:49.832Z","isFirstParty":false,"canonicalSlug":"training-assessments-string-normalisation-iqr-outlier-flags-28b218c6"}