{"uid":"cap_I67rhrtUl9uj8--K_Gc73","slug":"time-labels-adjacent-word-pair-frequency-a6251226","name":"Time Labels — Adjacent Word-Pair Frequency","description":"Time labels — Adjacent word-pair frequency: Adjacent word-pair frequency for Time labels. One public HTTPS page or supplied HTML; static parsing, no JavaScript rendering, login or access bypass. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one bounded adjacent word-pair frequency result.","url":"https://market.datapackvibe.com/x402/assistant-bx-web-times-bigrams?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"url":{"type":"string","default":"","maxLength":2048},"html":{"type":"string","default":"<!doctype html><html><head></head><body><time id=\"sample\" data-kind=\"example\">Example product guide</time></body></html>","maxLength":100000},"term":{"type":"string","default":"product","maxLength":100,"minLength":1},"attribute":{"type":"string","default":"id","pattern":"^[a-zA-Z][a-zA-Z0-9:_-]{0,49}$","maxLength":50},"previous_texts":{"type":"array","items":{"type":"string","default":"","maxLength":1000},"default":["Older example"],"maxItems":200,"minItems":0},"attribute_value":{"type":"string","default":"sample","maxLength":200}}},"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_IUS_9AiJO7QjeaXvo6LV5","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":"Extracts bigram (adjacent word-pair) frequencies from HTML <time> elements on a given webpage or supplied HTML snippet.","exampleAgentPrompt":"Analyze the adjacent word-pair frequency in the <time> elements on https://example.com/blog — look for the term 'product' in elements where the id attribute equals 'sample', and compare against these previous texts: ['Older example'].","exampleUseCases":[{"title":"Blog post timestamp text patterns","prompt":"Can you pull the adjacent word-pair frequencies from the <time> labels on https://myblog.com/archive and find how often the term 'published' appears near other words in those elements?"},{"title":"E-commerce event date label analysis","prompt":"I have this HTML snippet with <time> tags for event listings — can you count the bigrams in those time elements, filtering for the data-kind attribute equal to 'event', and look for the term 'sale'?"},{"title":"News site temporal label NLP preprocessing","prompt":"Analyze the word-pair frequencies inside all <time> elements on https://newssite.com/articles, searching for the term 'breaking' in elements where the class attribute equals 'timestamp', using these previous texts as baseline: ['Yesterday news update']."}],"resultDescription":"Returns bounded adjacent word-pair (bigram) frequency counts extracted from <time> HTML elements on the target page or supplied HTML, scoped to the specified attribute and term filter.","failureModes":["URL is inaccessible or returns non-200 status — no content to parse","HTML content exceeds 100,000 character limit — request rejected","No <time> elements found matching the specified attribute/value — empty result","Term not found in any time element text — zero-frequency result","Invalid attribute name pattern — schema validation error"],"whenToPreferThis":"Choose this endpoint when you specifically need bigram (adjacent word-pair) frequency analysis scoped to HTML <time> elements, with optional attribute filtering. It is ideal for NLP preprocessing of temporal labels, blog timestamps, or event date text. Prefer it over general-purpose bigram tools when the target data lives inside <time> tags and you want bounded, cheap single-call results without JavaScript rendering.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T12:51:32.663Z","isFirstParty":false,"canonicalSlug":"time-labels-adjacent-word-pair-frequency-a6251226"}