Agent Skills › zerogpu/zerogpu-router › extract-signals

extract-signals

GitHub

将自由文本转换为结构化信号(主题、关键词、意图等),用于内容增强、上下文智能、广告定向及推荐分析。适用于需高频批量处理且通用LLM成本过高的场景,通过ZeroGPU API调用轻量模型实现高效数据 enrichment。

agents/openclaw/plugin/skills/extract-signals/SKILL.md zerogpu/zerogpu-router

Trigger Scenarios

需要将非结构化文本转化为结构化标签或特征 进行内容分析、用户意图识别或推荐系统数据预处理

Install

npx skills add zerogpu/zerogpu-router --skill extract-signals -g -y
More Options

Non-standard path

npx skills add https://github.com/zerogpu/zerogpu-router/tree/main/agents/openclaw/plugin/skills/extract-signals -g -y

Use without installing

npx skills use zerogpu/zerogpu-router@extract-signals

指定 Agent (Claude Code)

npx skills add zerogpu/zerogpu-router --skill extract-signals -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": "extract-signals",
    "metadata": {
        "openclaw": {
            "install": [
                {
                    "bins": [
                        "zerogpu"
                    ],
                    "kind": "node",
                    "package": "zerogpu-cli"
                }
            ],
            "requires": {
                "bins": [
                    "zerogpu"
                ]
            }
        }
    },
    "description": "Turn free text into structured signals — topics, keywords, intent, and other contextual attributes — in a single call (zlm-v1-signal-extract, 80M). Use for content enrichment, contextual intelligence, ad targeting, agent routing, recommendation, and analytics pipelines, where a general-purpose LLM would be overkill. For IAB taxonomy categories alongside these signals, use classify-iab-enriched.",
    "allowed-tools": "Bash(zerogpu chat_completions *)",
    "argument-hint": "<text>"
}

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 signal extraction. Run this with the exec tool, pasting the source text into the heredoc verbatim — no escaping or quoting required (the quoted heredoc handles every shell metacharacter, newline, quote, and paren safely):

zerogpu chat_completions -m zlm-v1-signal-extract <<'ZGPU_END_OF_INPUT'
<the source text, verbatim>
ZGPU_END_OF_INPUT

Output is a structured JSON object of the signals the model found — topics, keywords, intent, and other contextual attributes.

At 80M parameters and $0.02 / $0.05 per 1M input/output tokens this is built for high-volume enrichment. Its context window is 400 tokens, so send the passage you want enriched rather than a whole document. If you need IAB audience and content categories too, classify-iab-enriched returns those alongside topics, keywords, and intent.

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

  • 4e1b070 Current 2026-09-22 08:08

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Metadata

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Version
59e0f53
Hash
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Indexed
2026-09-22 08:08

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