Intelligent multi-engine search with automatic network detection and fallback prioritization.
Works with
Supports four search engines with two priority modes: quality-first (Tavily > DuckDuckGo > Bing API > Bing scraper) or balanced/free-first (DuckDuckGo > Tavily > Bing API > Bing scraper)
Includes automatic quota management for API-based engines, 5-minute network detection caching, and forced re-detection after network changes
Provides web content fetching and batch enrichment of search resul
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmulti-searchExecute the skills CLI command in your project's root directory to begin installation:
Fetches multi-search from nex-zmh/agent-websearch-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate multi-search. Access via /multi-search in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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本技能整合多个搜索引擎，自动检测网络环境，智能选择最佳可用引擎。
from multi_search import search
# 平衡模式 - 优先免费引擎
results = search("Python tutorial", max_results=5)
# 质量优先模式 - 优先使用 Tavily
results = search("AI research", max_results=5, prefer_quality=True)
# 强制重新检测网络（切换 VPN 后使用）
results = search("OpenClaw skills", max_results=5, force_network_check=True)
from multi_search import search_skills
results = search_skills("OpenClaw AI agent automation", max_results=10)
from multi_search import get_status
status = get_status() # 使用缓存
status = get_status(force_network_check=True) # 强制重新检测
from multi_search import search, fetch_web_content, fetch_search_results_content
# 搜索并抓取第一个结果的详细内容
results = search("OpenClaw new features", max_results=3)
if results:
content = fetch_web_content(results[0]['href'], max_length=3000)
# content['title'], content['content'], content['success']
# 批量抓取所有搜索结果的详细内容
enriched_results = fetch_search_results_content(results, max_length=2000)
for r in enriched_results:
if r.get('full_content'):
# 使用 summarize 技能总结内容
pass
OpenClaw 工作流：
1. 使用 multi-search 搜索关键词
2. 选择感兴趣的搜索结果
3. 使用 fetch_web_content() 抓取网页内容
4. 使用 summarize 技能总结网页内容
5. 将摘要呈现给用户
[
{
'title': '结果标题',
'href': 'https://example.com',
'body': '结果摘要...',
'source': 'duckduckgo' # 或 'tavily', 'bing_api', 'bing_scraper'
}
]
query: 搜索关键词max_results: 最大结果数（默认5）prefer_quality: 是否优先质量（默认False）force_network_check: 是否强制重新检测网络（默认False）Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
kostja94/marketing-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
We added multi-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
multi-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: multi-search is focused, and the summary matches what you get after install.
Useful defaults in multi-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
multi-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
multi-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for multi-search matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: multi-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
multi-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
multi-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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