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zerogpu-router
zerogpu-router contains 28 collected skills from zerogpu, with repository-level occupation coverage and site-owned skill detail pages.
Skills in this repository
Show how much you've saved by routing tasks to ZeroGPU instead of your frontier model — a rounded dollar estimate and the frontier-model tokens offloaded. Use when the user asks how much they've saved, their ZeroGPU savings, or to see the cost-savings summary.
Sign in to ZeroGPU and persist the API key. Use when the user asks to log in, authenticate, or set up ZeroGPU credentials for the first time.
Sign in to ZeroGPU and persist the API key. Use when the user asks to log in, authenticate, or set up ZeroGPU credentials for the first time.
Zero-shot classification against a caller-supplied list of candidate labels (deberta-v3-small). Use when the user wants to classify text into a custom set of labels they provide (e.g. "is this positive, negative, or neutral?", "tag this as bug, feature, or question").
Short chat reply via the ZeroGPU edge model (LFM2.5-1.2B-Instruct). Use when the user wants a quick, single-turn answer that does not need host-level reasoning, prior conversation context, or code generation. Optional system instructions via -i.
Chat with ZeroGPU's Thinking variant (LFM2.5-1.2B-Thinking), which returns visible reasoning alongside the answer. Use when the user wants the model's reasoning shown, or asks a short logic/math/word-problem question that benefits from step-by-step output.
Enriched IAB classification — returns audience categories plus topics, keywords, and inferred intent. Use when the user wants richer ad/audience signals than plain IAB labels (e.g. "give me topics, keywords, and intent for this passage").
Classify text against the IAB content/audience taxonomy. Use when the user asks for IAB categories, ad-taxonomy labels, or "what topic is this article about" against a standard taxonomy.
Multi-axis classification using a JSON schema mapping categories to allowed labels (gliner2-base-v1). Use when the user wants to classify text along several dimensions at once, e.g. "classify by sentiment and topic" with explicit label sets per axis.
Zero-shot classification against a caller-supplied list of candidate labels (deberta-v3-small). Use when the user wants to classify text into a custom set of labels they provide (e.g. "is this positive, negative, or neutral?", "tag this as bug, feature, or question").
Show how much you've saved by routing tasks to ZeroGPU instead of the host model — cumulative dollars and tokens offloaded. Use when the user asks how much they've saved, their ZeroGPU savings, or to see the cost-savings summary.
Custom-label named-entity recognition (gliner2-base-v1). Use when the user wants to extract entities with their own labels — people, organizations, locations, products, dates, or any caller-defined entity types — from a passage.
Schema-driven structured JSON extraction (gliner2-base-v1). Use when the user wants to pull specific named fields out of free text into a JSON object — contact info, invoice details, order data, profile attributes — defined by a per-field type/description schema.
Extract PII entities from text (gliner-multi-pii-v1). Use when the user wants to find personally identifiable information — names, emails, phones, addresses, financial identifiers — grouped by category, without modifying the source text.
Detect and mask PII in-line in the text, replacing it with label placeholders like [PERSON] and [EMAIL]. Use when the user asks to redact, scrub, mask, anonymize, or sanitize a passage before sharing or logging it.
Show current ZeroGPU sign-in status and the masked API key. Use when the user asks whether they are logged in, who they are signed in as, or to verify credentials.
Summarize a passage using ZeroGPU's llama-3.1-8b-instruct-fast edge model. Use when the user asks to summarize, condense, TL;DR, or give the gist of an article, email, transcript, or other plain-text passage.
Short chat reply via the ZeroGPU edge model (LFM2.5-1.2B-Instruct). Use when the user wants a quick, single-turn answer that does not need Claude-level reasoning, prior conversation context, or code generation. Optional system instructions via -i.
Chat with ZeroGPU's Thinking variant (LFM2.5-1.2B-Thinking), which returns visible reasoning alongside the answer. Use when the user wants the model's reasoning shown, or asks a short logic/math/word-problem question that benefits from step-by-step output.
Enriched IAB classification — returns audience categories plus topics, keywords, and inferred intent. Use when the user wants richer ad/audience signals than plain IAB labels (e.g. "give me topics, keywords, and intent for this passage").
Classify text against the IAB content/audience taxonomy. Use when the user asks for IAB categories, ad-taxonomy labels, or "what topic is this article about" against a standard taxonomy.
Multi-axis classification using a JSON schema mapping categories to allowed labels (gliner2-base-v1). Use when the user wants to classify text along several dimensions at once, e.g. "classify by sentiment and topic" with explicit label sets per axis.
Custom-label named-entity recognition (gliner2-base-v1). Use when the user wants to extract entities with their own labels — people, organizations, locations, products, dates, or any caller-defined entity types — from a passage.
Schema-driven structured JSON extraction (gliner2-base-v1). Use when the user wants to pull specific named fields out of free text into a JSON object — contact info, invoice details, order data, profile attributes — defined by a per-field type/description schema.
Extract PII entities from text (gliner-multi-pii-v1). Use when the user wants to find personally identifiable information — names, emails, phones, addresses, financial identifiers — grouped by category, without modifying the source text.
Detect and mask PII in-line in the text, replacing it with label placeholders like [PERSON] and [EMAIL]. Use when the user asks to redact, scrub, mask, anonymize, or sanitize a passage before sharing or logging it.
Summarize a passage using ZeroGPU's llama-3.1-8b-instruct-fast edge model. Use when the user asks to summarize, condense, TL;DR, or give the gist of an article, email, transcript, or other plain-text passage.
Show current ZeroGPU sign-in status and the masked API key. Use when the user asks whether they are logged in, who they are signed in as, or to verify credentials.