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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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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").
| name | cost-savings |
| description | 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. |
| disable-model-invocation | true |
| allowed-tools | Bash(zerogpu cost_savings*) |
Show the ZeroGPU cost-savings summary:
zerogpu cost_savings
Relay the report to the user as-is. Token counts are actual (from the API); the dollar figure is a rounded estimate of what the same work would have cost on your frontier model (default baseline claude-opus-4-8, overridable via the ZEROGPU_SAVINGS_MODEL env var). Pass --json for raw data or --reset to clear the history.