General Utilities x402 offerings

Purpose-bound Shopify observation (shop) using the existing fresh Stack-Monitor. Returns stack_shopify_decision, direct versus inferred status, evidence, optional version difference against a caller-owned baseline and reusable snapshot. Missing fingerprints do not prove absence. No background scheduling, CVE, compatibility or installed-extension inventory. Existing outbound safeguards remain in the injected motor. One public HTTPS root domain, up to150 baseline technologies; 9s timeout.

Pre-attack intent signals from Telegram: access sales, 0days, ransomware targeting. Filters: sector, country, organization, intent_type(access_sale|0day|ransomware|exploit), limit. Signals appear before attacks.

Upload files to temporary storage for short-term sharing.

Prüft am Objekt iframe den Befund übertragene Bytemenge.

Prüft am Objekt skript den Befund übertragene Bytemenge.

Prüft am Objekt weiterleitung den Befund übertragene Bytemenge.

Prüft am Objekt dokument den Befund Zahl geladener Ressourcen.

Prüft am Objekt bild den Befund übertragene Bytemenge.

Prüft am Objekt seite den Befund Anteil moderner Bildformate.

Prüft am Objekt skript den Befund Zahl geladener Ressourcen.

Prüft am Objekt seite den Befund übertragene Bytemenge.

Prüft am Objekt schrift den Befund übertragene Bytemenge.

Prüft am Objekt schrift den Befund übertragene Bytemenge.

Prüft am Objekt iframe den Befund Anteil moderner Bildformate.

Transform text: URL slug, remove accents, ASCII, snake_case, camelCase, PascalCase, kebab-case, CONSTANT_CASE, Title, upper, lower, reverse, strip HTML, normalize whitespace and Unicode (NFC/NFKC). One or all at once.

Split long text into overlapping chunks for RAG and embeddings, by characters, words or estimated tokens, keeping sentences or paragraphs whole, with each chunk's position and size.

Classify text into YOUR labels in one call. Body: {text (<=4000 utf-8 bytes), labels (2-20 strings)}. Returns {label} — always exactly one of your labels. Flat price, no prompt engineering needed. Use in loops for routing, moderation queues, lead triage, sentiment buckets, intent detection.

Compare two texts by lines or words: similarity %, added and removed items, list of changes with positions, and a unified diff. For contract versions, prompts, configs or scraped pages.

Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingual input works best in English) for semantic search, clustering and deduplication, or rerank mode: pass query + documents to get documents sorted by cosine similarity. Runs locally, texts are not stored. Params: texts (1 to 32 strings, max 2000 chars each) for embed mode, or query + documents (up to 64) and top_k for rerank mode.

Analyzes text and returns core metrics like counts and readability insights.

Similarity between two texts without AI: TF-IDF cosine, vocabulary Jaccard and character-trigram Jaccard (catches edited copies), shared words and a verdict. For duplicates, plagiarism checks and deduplication.

Counts for any text in one call: characters (Unicode code points), words, sentences, paragraphs, avgWordLength, readingTimeMinutes (200 words a minute) and estimatedTokens, a rough LLM token estimate (about 4 characters a token, one per Chinese or Japanese character). Chinese and Japanese text, which has no spaces, is counted a character per word, and 。！？ end sentences.

Text statistics without AI: characters, words, sentences, paragraphs, unique words, reading and speaking time, readability (Flesch / Flesch-Kincaid for English, Fernandez-Huerta for Spanish) and most frequent words.

Formuliert Text um: klarer, kürzer, formell, locker oder SEO-optimiert (Rewrite text: clearer, shorter, formal, casual or SEO).