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brave-search勇敢的寻找

Agent Skill

brave-search 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

196

周安装

8

GitHub Stars

6

下载量

63
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:brave-search(勇敢的寻找)
来源仓库:https://github.com/mikeng-io/agent-skills
仓库路径:skills/brave-search
安装命令:
npx skills add https://github.com/mikeng-io/agent-skills --skill brave-search
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mikeng-io/agent-skills --skill brave-search

简介

brave-search 用于查找、检索和筛选相关信息。

  • 适合根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定仓库安装并使用该技能。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Brave Search MCP — Optional Web Search Enrichment

This skill wraps the Brave Search MCP server to provide structured web, news, and local search capabilities. It is designed to be invoked by other skills or used standalone for general research.

Setup: Requires the Brave Search MCP server configured with a Brave Search API key.

# Add to MCP config (one-time setup)
claude mcp add -s user brave-search npx @brave/search-mcp
# Then set: BRAVE_API_KEY=your_key_here
# Get a free API key at: https://brave.com/search/api/

Pre-Flight: Check Availability

ToolSearch: "brave-search"
  → Returns: mcp__brave-search__brave_web_search (and news, local, etc.)
  → If found: proceed to Step 1
  → If not found: return availability: "unavailable", fallback to other search tools

Non-blocking fallback chain: Brave Search → web-search-prime → WebFetch → Bash curl/wget


Step 1: Select Search Mode

Choose the appropriate Brave Search tool based on query type:

search_mode_selection:
  brave_web_search:
    trigger: General research, technical questions, documentation lookup
    tool: mcp__brave-search__brave_web_search
    returns: Web results with titles, URLs, descriptions

  brave_news_search:
    trigger: Recent events, breaking news, time-sensitive topics, announcements
    tool: mcp__brave-search__brave_news_search
    returns: News articles with publication dates and source attribution

  brave_local_search:
    trigger: Location-specific queries, "near me", city/region-scoped research
    tool: mcp__brave-search__brave_local_search
    returns: Local business/event results with location data

  brave_summarizer:
    trigger: "Summarize what the web says about X" — AI-distilled summary of top results
    tool: mcp__brave-search__brave_summarizer
    returns: Synthesized summary (does not return individual URLs — use when summary is enough)

  brave_video_search:
    trigger: Tutorial videos, conference talks, demo recordings
    tool: mcp__brave-search__brave_video_search

  brave_image_search:
    trigger: Visual content, diagrams, charts, screenshots
    tool: mcp__brave-search__brave_image_search

Step 2: Execute Queries

Run 1-5 queries depending on research scope. Parallel execution is fine for independent queries.

Query best practices:

  • Be specific: "Kafka consumer group rebalancing Go" not just "Kafka"
  • Use quotes for exact phrases: "distributed tracing" "OpenTelemetry"
  • Add year for recent content: OpenTelemetry Go 2025
  • For news: use brave_news_search with a shorter, more topic-focused query

Example usage:

# Web search
mcp__brave-search__brave_web_search(query="OpenTelemetry Go SDK distributed tracing 2025", count=10)

# News search
mcp__brave-search__brave_news_search(query="Kubernetes security CVE 2025", count=5)

# Local search
mcp__brave-search__brave_local_search(query="blockchain conferences San Francisco 2025")

Step 3: Process Results

For each result, extract and structure:

{
  "source": "brave-search",
  "search_mode": "web | news | local | summarizer | video | image",
  "query": "the query executed",
  "results": [
    {
      "title": "Result title",
      "url": "https://actual-content-url",
      "description": "Excerpt or description",
      "published_date": "2025-01-15",    // news results
      "credibility": "HIGH | MEDIUM | LOW",
      "key_points": ["Extracted insight 1", "Extracted insight 2"]
    }
  ],
  "availability": "available",
  "result_count": 10
}

Credibility assessment:

  • HIGH: Official docs, academic papers, recognized industry authorities
  • MEDIUM: Established company blogs, tech publications, conference proceedings
  • LOW: Personal blogs, forums, social media, unverified sources

Calling Context Integration

When invoked by deep-research

Return structured results for integration into the domain researcher's findings. Multi-search strategy: run Brave Web and Brave News in parallel for each domain query to get broader coverage.

When invoked by context skill (optional enrichment)

Use brave_web_search with 1-2 queries to detect current technology patterns, framework versions, or industry terminology signals that supplement domain selection.

When invoked standalone

Execute queries, return structured results with source attribution. Suitable for quick research tasks that don't require the full deep-research pipeline.


Output

{
  "skill": "brave-search",
  "availability": "available | unavailable",
  "queries_executed": ["list of queries"],
  "search_modes_used": ["web", "news"],
  "results": [...],
  "result_count": 15,
  "fallback_used": false
}

If unavailable:

{
  "skill": "brave-search",
  "availability": "unavailable",
  "reason": "MCP server not configured",
  "setup_hint": "claude mcp add -s user brave-search npx @brave/search-mcp"
}

Why Brave Search?

  • Independent index: Not Google — provides genuine search diversity
  • Privacy-first: No tracking or personalization affecting results
  • API quality: Structured JSON responses with rich metadata
  • News freshness: Separate news endpoint with publication dates
  • No rate limiting drama: Paid API with predictable quotas
  • Local search: Useful for geo-specific research without extra configuration

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.38%
按下载量换算22

Claude

31.59%
按下载量换算20

Cursor

17.26%
按下载量换算11

Gemini CLI

8.87%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

未通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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