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Exa Semantic Search

ASecurity

当需要基于语义/向量做网页检索、找相似内容或按类别(公司/人物/论文等)做结构化调研时使用;做 Exa API 语义搜索并产出带正文摘要的结果集;不适用于精确关键词匹配、站内全文检索或纯本地数据查询;触发词:语义搜索、相似内容、Exa

3 stars
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Added 9/19/2026
ai-agentsbashgitapi

Works with

claude codecursorcliapi

Security Analysis

A96/100
mediumUses curl or wget to download content

Pro shows the line behind each finding and how to fix it

Scanned 9/19/2026

$npx -y skills add findscripter/everything-skills --skill exa-semantic-search --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
name: exa-semantic-search
title: Exa 语义搜索研究
description: 当需要基于语义/向量做网页检索、找相似内容或按类别(公司/人物/论文等)做结构化调研时使用;做 Exa API 语义搜索并产出带正文摘要的结果集;不适用于精确关键词匹配、站内全文检索或纯本地数据查询;触发词:语义搜索、相似内容、Exa
domain: 平台/integration
triggers: [语义搜索, 相似内容, Exa, 类别检索, 论文检索, embeddings 搜索]
tags: [平台, misc, 搜索, 调研, exa, 语义检索]
level: 入门
status: stable
agents: [claude-code, codex, cursor, gemini-cli]
tools: [Exa API, 环境变量, curl]
requires: []
related: [firecrawl-web-scraper, defuddle-web-extract, hybrid-search-retrieval, apify-ecommerce-scraper]
combines_with: [rag-implementation-workflow, defuddle-web-extract]
license: MIT
source: sickn33/agentic-awesome-skills
source_license: MIT
---
## 何时使用

适用:
- 需要基于语义/embeddings 检索网页,而非精确关键词匹配(例如"讲清楚 X 概念的优质文章")。
- 给定一篇文章/URL,想找语义相似的内容(find similar)。
- 按类别做结构化调研:公司、人物、研究论文、新闻、GitHub 等。
- 希望检索结果直接带回正文/摘要,省去逐条抓取。

不该用(负边界):
- 精确关键词或布尔匹配、站内全文检索 —— 用传统关键词搜索引擎或站点自带搜索。
- 纯本地/私有数据库查询 —— Exa 检索的是公网内容。
- 把结果当作权威事实直接采纳 —— 仍需对来源做环境相关的核验与人工审阅。
- 缺少 API Key、检索范围或成功标准不明确时,先停下来澄清。

## 步骤

1. 安装技能(全局):
   ```bash
   npx skills add -g BenedictKing/exa-search
   ```
2. 配置 Exa API Key(务必通过环境变量,不要硬编码):
   ```bash
   export EXA_API_KEY="your-exa-api-key"
   ```
3. 在 Claude Code 对话中用自然语言发起检索;按需指定类别(company / people / research paper 等)或"找相似"。
4. 拿到结果后,对关键结论回到原始来源做二次核验。

## 指令

- 语义搜索:描述你想要的内容含义,而非堆砌关键词。
- 类别检索:明确说明类别(如"研究论文""公司"),以获得结构化结果。
- 相似内容:提供一个参考 URL,请求"语义相似"的页面。
- 直接调用 API(无封装时的兜底):
  ```bash
  curl -s https://api.exa.ai/search \
    -H "x-api-key: $EXA_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"query":"best explanation of vector embeddings","numResults":5,"contents":{"text":true}}'
  ```

## 示例

- 语义搜索:"找几篇把向量 embeddings 讲清楚的高质量文章。"
- 类别检索:"按研究论文类别,找近一年关于检索增强生成(RAG)的代表性论文。"
- 相似内容:"基于这篇 URL,找语义相似的深度文章。"

更多用法与排障见上游仓库:https://github.com/BenedictKing/exa-search

## 注意事项

- API Key 一律走环境变量(EXA_API_KEY),不写入代码或提交记录。
- 语义检索结果具有概率性,重要结论需回到原始来源核验,不可作为环境相关验证、测试或专家评审的替代。
- 仅在任务确实落在上述语义/相似/类别检索范围内时使用;否则改用关键词搜索或对应专用工具。
- 检索消耗 API 配额,注意 numResults 与调用频率。

## 互见

- context7-auto-research(依赖库/框架文档自动调研)
- tavily-web(通用 Web 搜索)
- firecrawl-scraper(抓取与结构化提取网页)
- codex-review(代码审阅)

---
采编自 sickn33/antigravity-awesome-skills(MIT)。

Attribution

findscripterfindscripter
View sourceSee grades on GitHubMore from findscripter →
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