Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fanzhidongyzby/openclaw-serper --skill serper-searchgit clone --depth 1 https://github.com/fanzhidongyzby/openclaw-serperWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fanzhidongyzby/openclaw-serper/serper-search)<a href="https://agentmods.dev/skills/fanzhidongyzby/openclaw-serper/serper-search"><img src="https://agentmods.dev/badge/skills/fanzhidongyzby/openclaw-serper/serper-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fanzhidongyzby/openclaw-serper/serper-search"><img src="https://agentmods.dev/badge/skills/fanzhidongyzby/openclaw-serper/serper-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.02126 |
| Opus 5 | $0.00028 | $0.01063 |
| Sonnet 5 | $0.00011 | $0.00425 |
| Haiku 4.5 | $0.00006 | $0.00213 |
Grade A, and why
serper-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Serper Search Tool
基于 Google Serper API 的网页搜索工具,提供实时、准确的搜索结果。
When to Activate
当用户提到以下内容时自动激活:
搜索类关键词
- "搜索"、"搜一下"、"搜搜"、"查找"、"找一下"
- "研究"、"调研"、"了解"
- "查询"、"检索"
- "看看"、"查查"
特定场景
- 需要获取最新新闻、信息
- 需要验证事实或数据
- 需要研究某个技术或概念
- 需要查找文档、教程
- 需要比较不同产品或方案
示例问题
- "搜一下最新的 AI 发展趋势"
- "帮我搜索最新的 AI 发展趋势"
- "查找一下 Python 3.13 的新特性"
- "研究一下自动驾驶技术的现状"
- "查查最新的网络安全新闻"
- "找一些关于微服务的教程"
Tools
serper_search
用途: 执行网络搜索,返回结果列表
参数:
query(必选,string):搜索关键词num(可选,number):返回结果数量,默认 5,最大 20gl(可选,string):国家代码,默认 cn- 推荐值: cn(中国)、us(美国)、uk(英国)、jp(日本)
hl(可选,string):语言代码,默认 zh-CN- 推荐值: zh-CN(简体中文)、en(英文)、ja(日语)
Best Practices
1. 搜索技巧
使用具体的关键词,避免过于宽泛:
示例:
- ✅ "Kimi AI 模型 参数 对比 2025"
- ✅ "Python 3.13 新特性 官方文档"
- ❌ "Python"(太宽泛,结果太多)
2. 添加时间限定
明确时间范围,获取最新信息:
示例:
- ✅ "LangChain 最新文档 2025"
- ✅ "Python 3.13 发布时间"
- ✅ "AI 人工智能 新闻 2025年2月"
3. 使用精确搜索
用引号搜索精确短语:
示例:
- ✅ ""machine learning" 最佳实践"
- ✅ ""RAG 架构" 实现"
4. 添加技术术语
提高搜索精度:
示例:
- ✅ "Spring Cloud 微服务 实现"
- ✅ "React Hooks useEffect 使用"
5. 结果数量选择
根据需求调整:
- 快速查询:
num=3(快速浏览核心信息) - 深入研究:
num=10(全面了解主题) - 综合调研:
num=20(最大深度,覆盖面广)
6. 多轮搜索策略
对于复杂主题,使用多轮搜索深入挖掘:
场景:研究微服务架构
// 第一轮:基础概念
serper_search({ query: "微服务架构 概念 优势", num: 5 })
// 第二轮:技术实现
serper_search({ query: "微服务 Spring Cloud Kubernetes", num: 8 })
// 第三轮:最佳实践
serper_search({ query: "微服务 最佳实践 常见问题", num: 10 })
7. 结果处理原则
- 优先权威来源: 官方文档、知名技术博客、学术论文
- 交叉验证: 对比多个来源的信息
- 关注时效性: 优先较新的内容(近1-2年)
- 快速筛选: 通过 snippet 判断相关性
Example Scenarios
场景 1:技术调研
用户提问: "研究一下 LangChain 的核心概念"
AI 行为:
// 搜索核心概念
serper_search({
query: "LangChain 核心概念 官方文档 2025",
num: 5
})
// 如果需要更多细节,继续搜索
serper_search({
query: "LangChain Agents Chains 使用示例",
num: 8
})
回复示例:
根据搜索结果,LangChain 的核心概念包括:
1. **Chains(链)**:将多个组件串联成工作流
2. **Agents(智能体)**:自主决策的 AI 代理
3. **Tools(工具)**:LLM 可用的外部能力
4. **Memory(记忆)**:对话上下文管理
📖 详细文档:https://python.langchain.com/
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 300 lines · 55 tokens per session scan A b8ebe5b1c461
serper-search is a skill published in the GitHub repository fanzhidongyzby/openclaw-serper (4 stars, last pushed 7mo ago), licensed MIT. It adds 55 tokens to every session and 2,126 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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