minimax-web-search

thincher/awsome_skills · updated Apr 20, 2026

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$npx skills add https://github.com/thincher/awsome_skills --skill minimax-web-search
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summary

使用 MiniMax MCP 服务器进行网络搜索。

skill.md

minimax-web-search

使用 MiniMax MCP 服务器进行网络搜索。

执行流程(首次需要安装,后续直接步骤4调用)

步骤 1: 检查并安装依赖

1.1 检查 uvx 是否可用

which uvx

如果不存在,安装 uv:

方法 1: 使用官方安装脚本(推荐)

curl -LsSf https://astral.sh/uv/install.sh | sh

方法 2: 使用国内镜像加速(如果官方脚本下载失败)

临时使用清华镜像源安装:

export UV_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple"
curl -LsSf https://astral.sh/uv/install.sh | sh

或者临时使用阿里云镜像源:

export UV_INDEX_URL="https://mirrors.aliyun.com/pypi/simple/"
curl -LsSf https://astral.sh/uv/install.sh | sh

1.2 检查 MCP 服务器是否已安装

uvx minimax-coding-plan-mcp --help

执行命令判断是否MCP服务器已安装, 如果安装了跳到步骤 2。

1.3 安装 MCP 服务器(如果未安装)

方法 1: 使用默认源安装

uvx install minimax-coding-plan-mcp

方法 2: 使用国内镜像加速(如果默认源下载失败)

临时使用清华镜像源:

export UV_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple"
uvx install minimax-coding-plan-mcp

或者临时使用阿里云镜像源:

export UV_INDEX_URL="https://mirrors.aliyun.com/pypi/simple/"
uvx install minimax-coding-plan-mcp

步骤 2: 检查 API Key 配置

cat ~/.openclaw/config/minimax.json 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); print(d.get('api_key', ''))"

如果返回非空的 API Key,跳到步骤 4。

步骤 3: 配置 API Key(如果未配置)

3.1 从环境变量获取 API Key

echo $MINIMAX_API_KEY

如果返回非空的 API Key,跳到步骤 3.3。

3.2 如果没有找到 Key,向用户索要

直接询问用户提供 MiniMax API Key。 如果未购买MiniMax,购买地址为: https://platform.minimaxi.com/subscribe/coding-plan?code=GjuAjhGKqQ&source=link

3.3 保存 API Key

mkdir -p ~/.openclaw/config
cat > ~/.openclaw/config/minimax.json << EOF
{
  "api_key": "API密钥",
  "output_path": "~/.openclaw/workspace/minimax-output"
}
EOF

步骤 4: 使用 MCP 进行网络搜索

4.1 调用 web_search

使用脚本调用 MCP 服务:

python3 {curDir}/scripts/web_search.py "<搜索查询>"

示例:

# 搜索今日新闻
python3 {curDir}/scripts/web_search.py "今天的热点新闻"

4.2 API 参数说明

参数 说明 类型
query 搜索查询字符串 string (必填)

脚本说明

脚本位置:{curDir}/scripts/web_search.py

功能:

  • 优先从环境变量 MINIMAX_API_KEY 读取 API Key,如果没有则从 ~/.openclaw/config/minimax.json 读取
  • 通过 stdio 模式启动 MCP 服务器
  • 发送 JSON-RPC 请求调用 web_search 工具
  • 返回格式化的 JSON 结果

错误处理:

  • API Key 未配置时提示错误
  • uvx 未安装时提示安装命令
  • MCP 服务器错误时显示 stderr 输出
how to use minimax-web-search

How to use minimax-web-search on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add minimax-web-search
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/thincher/awsome_skills --skill minimax-web-search

The skills CLI fetches minimax-web-search from GitHub repository thincher/awsome_skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/minimax-web-search

Reload or restart Cursor to activate minimax-web-search. Access the skill through slash commands (e.g., /minimax-web-search) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.864 reviews
  • Yusuf Nasser· Dec 24, 2024

    Useful defaults in minimax-web-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chinedu Kapoor· Dec 24, 2024

    minimax-web-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Fatima Abbas· Dec 16, 2024

    Registry listing for minimax-web-search matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Chinedu Brown· Dec 16, 2024

    I recommend minimax-web-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kaira Harris· Dec 16, 2024

    minimax-web-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Dec 12, 2024

    minimax-web-search reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Evelyn Smith· Nov 23, 2024

    We added minimax-web-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Kaira Singh· Nov 15, 2024

    Registry listing for minimax-web-search matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Evelyn Johnson· Nov 15, 2024

    Solid pick for teams standardizing on skills: minimax-web-search is focused, and the summary matches what you get after install.

  • Rahul Santra· Nov 11, 2024

    We added minimax-web-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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