{"uid":"cap_NQDnUk3QcGBXdAsls8XoQ","slug":"kihustle-context-window-packer-55c49329","name":"KiHustle Context Window Packer","description":"Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.","url":"https://kihustle.tech/api/v1/context-window-packer","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"},"max_tokens":{"type":"integer"}}},"responseSchema":{"type":"json","example":{"result":"processed","status":"success"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_he8NmbAlyBl6jDLOAlWdH","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Packs and optimizes text to fit within a specified token limit for LLM context windows","exampleAgentPrompt":"Pack this article text into a context window of 2000 tokens so I can send it to my LLM: [paste text here].","exampleUseCases":[{"title":"Fitting long documents into LLM prompts","prompt":"I have this 10,000-word research paper and I need to fit it into a 4096-token context window — can you pack it down for me so I can send it to my AI model?"},{"title":"Batch content summarization prep","prompt":"I'm processing blog posts through an LLM that only accepts 1500 tokens — can you pack this blog post text to fit within that limit before I send it?"},{"title":"RAG pipeline context optimization","prompt":"Before I inject this retrieved document chunk into my prompt, can you make sure it fits within 800 tokens? Here's the raw text."}],"resultDescription":"A JSON object with 'result' containing the processed/packed text and a 'status' field indicating success. The output text has been trimmed or optimized to fit within the specified max_tokens constraint.","failureModes":["Text or max_tokens not provided — returns validation error","max_tokens value is zero or negative — likely causes error or empty result","Text already fits within token limit — may return unchanged with success","Service unavailable — non-200 HTTP response","Ambiguous behavior when token counting method is not specified (e.g. which tokenizer)"],"whenToPreferThis":"Use this endpoint when you need to programmatically fit arbitrary text into a fixed token budget before passing it to an LLM. It is especially useful in RAG pipelines, automated summarization workflows, or any agentic system that ingests variable-length content and must stay within a model's context window. Prefer this over manual truncation when you want a reproducible, API-callable solution with a simple input/output contract.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T07:06:22.052Z","isFirstParty":false}