classify-zero-shot

Agent Skills › zerogpu/zerogpu-router › classify-zero-shot

classify-zero-shot

GitHub

对用户提供文本进行零样本分类，支持自定义标签列表。通过ZeroGPU API调用deberta-v3-small模型，适用于情感分析、内容打标等场景。需严格遵循heredoc格式传递输入以确保特殊字符处理正确。

agents/openclaw/plugin/skills/classify-zero-shot/SKILL.md zerogpu/zerogpu-router

Trigger Scenarios

用户要求将文本分类到特定标签集合询问文本的情感倾向（如正/负/中性）需要对内容进行自定义打标

Install

npx skills add zerogpu/zerogpu-router --skill classify-zero-shot -g -y

More Options

Non-standard path

npx skills add https://github.com/zerogpu/zerogpu-router/tree/main/agents/openclaw/plugin/skills/classify-zero-shot -g -y

Use without installing

npx skills use zerogpu/zerogpu-router@classify-zero-shot

指定 Agent (Claude Code)

npx skills add zerogpu/zerogpu-router --skill classify-zero-shot -a claude-code -g -y

安装 repo 全部 skill

npx skills add zerogpu/zerogpu-router --all -g -y

预览 repo 内 skill

npx skills add zerogpu/zerogpu-router --list

SKILL.md

Frontmatter

{
    "name": "classify-zero-shot",
    "metadata": {
        "openclaw": {
            "install": [
                {
                    "bins": [
                        "zerogpu"
                    ],
                    "kind": "node",
                    "package": "zerogpu-cli"
                }
            ],
            "requires": {
                "bins": [
                    "zerogpu"
                ]
            }
        }
    },
    "description": "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\").",
    "allowed-tools": "Bash(zerogpu classify_zero_shot*)",
    "argument-hint": "<text> (-l <label>... | --labels a,b,c) [-t <0..1>]"
}

Sends your input to ZeroGPU's hosted API for inference — this is not local processing. Don't pass secrets, credentials, or regulated data you aren't cleared to share with a third party. See the plugin README's "Data & privacy" section.

Run zero-shot classification:

zerogpu classify_zero_shot $ARGUMENTS

Quoting (required, to survive shell parsing of arbitrary user text): format $ARGUMENTS with the input text wrapped via heredoc command substitution, then flags after. Inside the heredoc, paste the user's text verbatim — do not escape:

"$(cat <<'ZGPU_T'
<the input text, verbatim, multi-line and special chars all OK>
ZGPU_T
)" --labels a,b,c

At least one label is required — either repeat -l <label> or pass --labels a,b,c. Optional -t <threshold> filters labels below a confidence for multi-label output.

Savings note: only if the command output literally contains a line starting with 💰 ZeroGPU savings, append that exact line, unchanged, as the last line of your reply. If no such line is present, say nothing about savings and do not mention or suggest the cost-savings skill — this note is intentionally occasional, not shown every time.

Version History

cee9321 Current 2026-08-02 21:15
更新依赖库并修复安全漏洞
7b9b503 2026-07-24 11:48

Metadata

Files: 0
Version: cee9321
Hash: dddc1605
Indexed: 2026-07-24 11:48

classify-zero-shot