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Agent Skill · 研究检索

nimble-web-search(灵活的网络搜索)

nimble-web-search 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

nimble-web-search 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 2,429。

总安装 61,207周安装 2,476GitHub Stars 3下载 19,214核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.0

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:nimble-web-search(灵活的网络搜索)
来源仓库:https://github.com/ilchemla/nimble-web-search
安装命令:
openclaw skills install nimble-web-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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install nimble-web-search

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

由 Nimble Search API 提供支持的实时网络智能。使用 8 种专门的焦点模式(一般、编码、新闻、学术、购物、社交、地理、位置)执行智能网络搜索。当您需要搜索网络、查找当前信息、发现 URL、研究主题或收集最新数据时,此技能可以提供实时搜索结果。

使用场合:搜索信息、查找最新新闻、查找学术论文、搜索编码示例、查找购物结果、发现社交媒体帖子、研究主题或获取最新的实时数据。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install nimble-web-search;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

nimble-web-search 怎么安装?

在终端执行 openclaw skills install nimble-web-search。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

nimble-web-search 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

nimble-web-search 需要什么权限?

权限级别是接触敏感数据。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。

nimble-web-search 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

nimble-web-search 用的人多吗?

公开数据是累计安装 61,207 次,近一周安装 2,476 次,下载 19,214 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

nimble-web-search 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
nimble-web-search

灵活的网络搜索 · 本页

61,2073接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
nimble-web-search
description
>
license
MIT
metadata
version
0.1.0
author
Nimbleway
repository
https://github.com/Nimbleway/agent-skills

Nimble Web Search

Real-time web intelligence using Nimble Search API with specialized focus modes and AI-powered result synthesis.

Prerequisites

Nimble API Key Required - Get your key at https://www.nimbleway.com/

Configuration

Set the NIMBLE_API_KEY environment variable using your platform's method:

Claude Code:

// ~/.claude/settings.json
{
  "env": {
    "NIMBLE_API_KEY": "your-api-key-here"
  }
}

VS Code/GitHub Copilot:

  • Add to .github/skills/ directory in your repository
  • Or use GitHub Actions secrets for the copilot environment

Shell/Terminal:

export NIMBLE_API_KEY="your-api-key-here"

Any Platform: The skill checks for the NIMBLE_API_KEY environment variable regardless of how you set it.

API Key Validation

IMPORTANT: Before making any search request, verify the API key is configured:

# Check if API key is set
if [ -z "$NIMBLE_API_KEY" ]; then
  echo "❌ Error: NIMBLE_API_KEY not configured"
  echo ""
  echo "Get your API key: https://www.nimbleway.com/"
  echo ""
  echo "Configure using your platform's method:"
  echo "- Claude Code: Add to ~/.claude/settings.json"
  echo "- GitHub Copilot: Use GitHub Actions secrets"
  echo "- Shell: export NIMBLE_API_KEY=\"your-key\""
  echo ""
  echo "Do NOT fall back to other search tools - guide the user to configure first."
  exit 1
fi

Overview

Nimble Search provides real-time web intelligence with 8 specialized focus modes optimized for different types of queries. Get instant access to current web data with AI-powered answer generation, deep content extraction, URL discovery, and smart filtering by domain and date.

IMPORTANT: Always Specify These Parameters

When using this skill, always explicitly set the following parameters in your requests:

  • deep_search: Default to false for 5-10x faster responses

- Use false (FAST MODE - 1-3 seconds): For 95% of use cases - URL discovery, research, comparisons, answer generation - Use true (DEEP MODE - 5-15 seconds): Only when you specifically need full page content extracted for archiving or detailed analysis

  • focus: Default to "general" for broad searches

- Change to specific mode (coding, news, academic, shopping, social, geo, location) for targeted results

  • max_results: Default to 10 - Balanced speed and coverage

Performance Awareness: By explicitly setting deep_search: false, you're choosing fast mode and should expect results in 1-3 seconds. If you set deep_search: true, expect 5-15 seconds response time.

Quick Start

Use the wrapper script for the simplest experience:

# ALWAYS specify deep_search explicitly
./scripts/search.sh '{
  "query": "React hooks",
  "deep_search": false
}'

The script automatically handles authentication, tracking headers, and output formatting.

When to Use Each Mode

Use deep_search: false (FAST MODE - 1-3 seconds) - Default for 95% of cases:

  • ✅ Finding URLs and discovering resources
  • ✅ Research and topic exploration
  • ✅ Answer generation and summaries
  • ✅ Product comparisons
  • ✅ News monitoring
  • ✅ Any time you DON'T need full article text

Use deep_search: true (DEEP MODE - 5-15 seconds) - Only when specifically needed:

  • 📄 Archiving full article content
  • 📄 Extracting complete documentation
  • 📄 Building text datasets
  • 📄 Processing full page content for analysis

Decision Rule: If you're not sure, use deep_search: false. You can always re-run with true if needed.

Core Capabilities

Focus Modes

Choose the appropriate focus mode based on your query type:

  1. general - Default mode for broad web searches
  2. coding - Real-time access to technical documentation, code examples, programming resources
  3. news - Real-time news articles, current events, breaking stories
  4. academic - Research papers, scholarly articles, academic resources
  5. shopping - Real-time product searches, e-commerce results, price comparisons
  6. social - Real-time social media posts, discussions, trending community content
  7. geo - Location-based searches, geographic information
  8. location - Local business searches, place-specific queries

Search Features

LLM Answer Generation

  • Request AI-generated answers synthesized from search results
  • Powered by Claude for high-quality summaries
  • Include citations to source URLs
  • Best for: Research questions, topic overviews, comparative analysis

URL Discovery

  • Extract 1-20 most relevant URLs for a query
  • Useful for building reading lists and reference collections
  • Returns URLs with titles and descriptions
  • Best for: Resource gathering, link building, research preparation

Deep Content Extraction

  • Default (Recommended): deep_search=false - Fastest response, returns titles, descriptions, and URLs
  • Optional: deep_search=true - Slower, extracts full page content
  • Important: Most use cases work perfectly with deep_search=false (the default)
  • Available formats when deep_search=true: markdown, plain_text, simplified_html
  • Only enable deep search for: Detailed content analysis, archiving, or comprehensive text extraction needs

Domain Filtering

  • Include specific domains (e.g., github.com, stackoverflow.com)
  • Exclude domains to remove unwanted sources
  • Combine multiple domains for focused searches
  • Best for: Targeted research, brand monitoring, competitive analysis

Time Filtering

  • Recommended: Use time_range for real-time recency filtering (hour, day, week, month, year)
  • Alternative: Use start_date/end_date for precise date ranges (YYYY-MM-DD)
  • Note: time_range and date filters are mutually exclusive
  • Best for: Real-time news monitoring, recent developments, temporal analysis

Usage Patterns

All examples below use the ./scripts/search.sh wrapper for simplicity. For raw API usage, see the API Integration section.

Basic Search

Quick search in fast mode (ALWAYS specify deep_search explicitly):

./scripts/search.sh '{
  "query": "React Server Components tutorial",
  "deep_search": false
}'

For technical content, specify coding focus (still fast mode):

./scripts/search.sh '{
  "query": "React Server Components tutorial",
  "focus": "coding",
  "deep_search": false
}'

Research with AI Summary

Get synthesized insights from multiple sources (fast mode works great with answer generation):

./scripts/search.sh '{
  "query": "impact of AI on software development 2026",
  "deep_search": false,
  "include_answer": true
}'

Domain-Specific Search

Target specific authoritative sources (fast mode):

./scripts/search.sh '{
  "query": "async await patterns",
  "focus": "coding",
  "deep_search": false,
  "include_domains": ["github.com", "stackoverflow.com", "dev.to"],
  "max_results": 8
}'

Real-Time News Monitoring

Track current events and breaking news as they happen (fast mode):

./scripts/search.sh '{
  "query": "latest developments in quantum computing",
  "focus": "news",
  "deep_search": false,
  "time_range": "week",
  "max_results": 15,
  "include_answer": true
}'

Academic Research - Fast Mode (Recommended)

Find and synthesize scholarly content using fast mode:

./scripts/search.sh '{
  "query": "machine learning interpretability methods",
  "focus": "academic",
  "deep_search": false,
  "max_results": 20,
  "include_answer": true
}'

When to use deep mode: Only use "deep_search": true if you need full paper content extracted for archiving:

./scripts/search.sh '{
  "query": "machine learning interpretability methods",
  "focus": "academic",
  "deep_search": true,
  "max_results": 5,
  "output_format": "markdown"
}'

Note: Deep mode is 5-15x slower. Use only when specifically needed.

Real-Time Shopping Research

Compare products and current prices (fast mode):

./scripts/search.sh '{
  "query": "best mechanical keyboards for programming",
  "focus": "shopping",
  "deep_search": false,
  "max_results": 10,
  "include_answer": true
}'

Parallel Search Strategies

When to Use Parallel Searches

Run multiple real-time searches in parallel when:

  • Comparing perspectives: Search the same topic across different focus modes
  • Multi-faceted research: Investigate different aspects of a topic simultaneously
  • Competitive analysis: Search multiple domains or competitors at once
  • Real-time monitoring: Track multiple topics or keywords concurrently
  • Cross-validation: Verify information across different source types in real-time

Implementation Methods

Method 1: Background Processes (Recommended)

Run multiple searches concurrently using the wrapper script:

# Start multiple searches in parallel
./scripts/search.sh '{"query": "React", "focus": "coding"}' > react_coding.json &
./scripts/search.sh '{"query": "React", "focus": "news"}' > react_news.json &
./scripts/search.sh '{"query": "React", "focus": "academic"}' > react_academic.json &

# Wait for all to complete
wait

# Combine results
jq -s '.' react_*.json > combined_results.json

Method 2: Loop with xargs (Controlled Parallelism)

Process multiple queries with rate limiting:

# Create queries file
cat > queries.txt <<EOF
{"query": "AI frameworks", "focus": "coding"}
{"query": "AI regulation", "focus": "news"}
{"query": "AI research", "focus": "academic"}
EOF

# Run with max 3 parallel processes
cat queries.txt | xargs -n1 -P3 -I{} ./scripts/search.sh '{}'

Method 3: Focus Mode Comparison

Search the same query across different focus modes:

QUERY="artificial intelligence trends"

for focus in "general" "coding" "news" "academic"; do
  (
    ./scripts/search.sh "{\"query\": \"$QUERY\", \"focus\": \"$focus\"}" \
      > "${focus}_results.json"
  ) &
done

wait
echo "All searches complete!"

Best Practices for Parallel Execution

  1. Rate Limiting: Limit parallel requests to 3-5 to avoid overwhelming the API

- Use xargs -P3 to set maximum concurrent requests - Check your API tier limits before increasing parallelism

  1. Error Handling: Capture and handle failures gracefully
   ./scripts/search.sh '{"query": "test"}' || echo "Search failed" >> errors.log
  1. Result Aggregation: Combine results after all searches complete
   # Wait for all searches
   wait

   # Merge JSON results
   jq -s 'map(.results) | flatten' result*.json > combined.json
  1. Progress Tracking: Monitor completion status
   echo "Running 5 parallel searches..."

   for i in {1..5}; do
     ./scripts/search.sh "{\"query\": \"query$i\"}" > "result$i.json" &
   done

   wait
   echo "All searches complete!"

Example: Multi-Perspective Research

#!/bin/bash
# Research a topic across multiple focus modes simultaneously

QUERY="artificial intelligence code generation"
OUTPUT_DIR="./search_results"
mkdir -p "$OUTPUT_DIR"

# Run searches in parallel across different focus modes
for focus in "general" "coding" "news" "academic"; do
  (
    ./scripts/search.sh "{
      \"query\": \"$QUERY\",
      \"focus\": \"$focus\",
      \"max_results\": 10
    }" > "$OUTPUT_DIR/${focus}_results.json"
  ) &
done

# Wait for all searches to complete
wait

# Aggregate and analyze results
jq -s '{
  general: .[0].results,
  coding: .[1].results,
  news: .[2].results,
  academic: .[3].results
}' "$OUTPUT_DIR"/*.json > "$OUTPUT_DIR/combined_analysis.json"

echo "✓ Multi-perspective search complete"

Performance Considerations

  • Optimal Parallelism: 3-5 concurrent requests balances speed and API limits
  • Memory Usage: Each parallel request consumes memory; monitor for large result sets
  • Network Bandwidth: Parallel requests can saturate bandwidth on slow connections
  • API Costs: More parallel requests = faster API quota consumption

When NOT to Use Parallel Searches

  • Single, focused query with one clear answer
  • Sequential research where each search informs the next
  • API quota is limited or expensive
  • Results need to be processed before next search
  • Simple URL collection that doesn't require multiple perspectives

API Integration

Note: For most use cases, use the ./scripts/search.sh wrapper script shown in Usage Patterns. The raw API examples below are for advanced users who need direct API access or custom integration.

Required Configuration

Before making any API request, always validate the API key is configured:

# Validate API key is set
if [ -z "$NIMBLE_API_KEY" ]; then
  echo "❌ Nimble API key not configured."
  echo "Get your key at https://www.nimbleway.com/"
  echo ""
  echo "Set NIMBLE_API_KEY environment variable using your platform's method."
  exit 1
fi

The skill requires the NIMBLE_API_KEY environment variable. See Prerequisites for platform-specific setup instructions.

Get your API key at: https://www.nimbleway.com/

API Endpoint

POST https://nimble-retriever.webit.live/search

Request Format

{
  "query": "search query string",  // REQUIRED
  "focus": "general",  // OPTIONAL: default "general" | coding|news|academic|shopping|social|geo|location
  "max_results": 10,  // OPTIONAL: default 10 (range: 1-100)
  "include_answer": false,  // OPTIONAL: default false
  "deep_search": false,  // OPTIONAL: default false (RECOMMENDED: keep false for speed)
  "output_format": "markdown",  // OPTIONAL: default "markdown" | plain_text|simplified_html
  "include_domains": ["domain1.com"],  // OPTIONAL: default [] (no filter)
  "exclude_domains": ["domain3.com"],  // OPTIONAL: default [] (no filter)
  "time_range": "week",  // OPTIONAL: hour|day|week|month|year
  "start_date": "2026-01-01",  // OPTIONAL: Use time_range OR start_date/end_date (not both)
  "end_date": "2026-12-31"  // OPTIONAL
}

Key Defaults:

  • focus: "general" - Change to specific mode for targeted results
  • deep_search: false - Keep false unless you need full page content
  • max_results: 10 - Balanced speed and coverage

Response Format

{
  "results": [
    {
      "url": "https://example.com/page",
      "title": "Page Title",
      "description": "Page description",
      "content": "Full page content (if deep_search=true)",
      "published_date": "2026-01-15"
    }
  ],
  "include_answer": "AI-generated summary (if include_answer=true)",
  "urls": ["url1", "url2", "url3"],
  "total_results": 10
}

Best Practices

Focus Mode Selection

Use coding for:

  • Programming questions
  • Technical documentation
  • Code examples and tutorials
  • API references
  • Framework guides

Use news for:

  • Real-time current events
  • Breaking stories as they happen
  • Recent announcements
  • Trending topics
  • Time-sensitive information

Use academic for:

  • Research papers
  • Scholarly articles
  • Scientific studies
  • Academic journals
  • Citations and references

Use shopping for:

  • Product searches
  • Price comparisons
  • E-commerce research
  • Product reviews
  • Buying guides

Use social for:

  • Real-time social media monitoring
  • Live community discussions
  • Current user-generated content
  • Trending hashtags and topics
  • Real-time public sentiment

Use geo for:

  • Geographic information
  • Regional data
  • Maps and locations
  • Area-specific queries

Use location for:

  • Local business searches
  • Place-specific information
  • Nearby services
  • Regional recommendations

Result Limits

  • Quick searches: 5-10 results for fast overview
  • Comprehensive research: 15-20 results for depth
  • Answer generation: 10-15 results for balanced synthesis
  • URL collection: 20 results for comprehensive resource list

When to Use LLM Answers

✅ Use LLM answers when:

  • You need a synthesized overview of a topic
  • Comparing multiple sources or approaches
  • Summarizing recent developments
  • Answering specific questions
  • Creating research summaries

❌ Skip LLM answers when:

  • You just need a list of URLs
  • Building a reference collection
  • Speed is critical
  • You want to analyze sources manually
  • Original source text is needed

Content Extraction

Default (Recommended): deep_search=false

The default setting works for 95% of use cases:

  • ✅ Fastest response times
  • ✅ Returns titles, descriptions, URLs
  • ✅ Works perfectly with include_answer=true
  • ✅ Sufficient for research, comparisons, and URL discovery

Only use deep_search=true when you specifically need:

  • Full page content extraction
  • Archiving complete articles
  • Processing full text for analysis
  • Building comprehensive datasets

Performance impact:

  • deep_search=false: ~1-3 seconds
  • deep_search=true: ~5-15 seconds (significantly slower)

Error Handling

Common Issues

Authentication Failed

  • Verify NIMBLE_API_KEY is set correctly
  • Check API key is active at nimbleway.com
  • Ensure key has search API access

Rate Limiting

  • Reduce max_results
  • Add delays between requests
  • Check your plan limits
  • Consider upgrading API tier

No Results

  • Try different focus mode
  • Broaden search query
  • Remove domain filters
  • Adjust date filters

Timeout Errors

  • Reduce max_results
  • Disable deep content extraction
  • Simplify query
  • Try again after brief delay

Performance Tips

  1. Use Defaults: Keep deep_search=false (default) for 5-10x faster responses
  2. Start Simple: Begin with just {"query": "..."} - defaults work great
  3. Choose Right Focus: Proper focus mode dramatically improves relevance (default: "general")
  4. Optimize Result Count: Default of 10 results balances speed and coverage
  5. Domain Filtering: Pre-filter sources for faster, more relevant results
  6. Avoid Deep Search: Only enable deep_search=true when you truly need full content
  7. Batch Queries: Group related searches to minimize API calls
  8. Cache Results: Store results locally when appropriate

Integration Examples

See the examples/ directory for detailed integration patterns:

  • basic-search.md - Simple search implementation
  • deep-research.md - Multi-step research workflow
  • competitive-analysis.md - Domain-specific research pattern

See references/ directory for detailed documentation:

  • focus-modes.md - Complete focus mode guide
  • search-strategies.md - Advanced search patterns
  • api-reference.md - Full API documentation

Scripts

search.sh - Main Search Wrapper

The recommended way to use the Nimble Search API:

./scripts/search.sh '{"query": "your search", "focus": "coding"}'

Features:

  • Automatic authentication with $NIMBLE_API_KEY
  • Platform detection (claude-code, github-copilot, vscode, cli)
  • Request tracking headers for analytics
  • JSON validation and error handling
  • Formatted output with jq

Usage:

# Basic search
./scripts/search.sh '{"query": "React hooks"}'

# With all options
./scripts/search.sh '{
  "query": "AI frameworks",
  "focus": "coding",
  "max_results": 15,
  "include_answer": true,
  "include_domains": ["github.com"]
}'

validate-query.sh - API Configuration Test

Test your API configuration and connectivity:

./scripts/validate-query.sh "test query" general

This verifies:

  • API key is configured
  • Endpoint is accessible
  • Response format is correct
  • Focus mode is supported

平台分布

OpenClaw

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安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

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权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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