research-lookup by davila7/claude-code-templates
npx skills add https://github.com/davila7/claude-code-templates --skill research-lookup此技能通过 OpenRouter 使用 Perplexity 的 Sonar 模型实现实时研究信息查询。它能根据查询复杂度智能选择 Sonar Pro Search(快速高效查询)和 Sonar Reasoning Pro(深度分析推理)。该技能提供对当前学术文献、最新研究、技术文档和一般研究信息的访问,并附带适当的引用和来源归属。
在以下情况下使用此技能:
使用此技能创建文档时,请始终考虑添加科学图表和示意图以增强视觉传达效果。
如果您的文档尚未包含示意图或图表:
对于新文档:默认应生成科学示意图,以可视化呈现文本中描述的关键概念、工作流程、架构或关系。
如何生成示意图:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
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AI 将自动:
何时添加示意图:
有关创建示意图的详细指南,请参阅 scientific-schematics 技能文档。
搜索学术文献:查询特定领域的最新论文、研究和综述:
查询示例:
- "Recent advances in CRISPR gene editing 2024"
- "Latest clinical trials for Alzheimer's disease treatment"
- "Machine learning applications in drug discovery systematic review"
- "Climate change impacts on biodiversity meta-analysis"
预期响应格式:
协议和方法查找:查找详细程序、规范和方法学:
查询示例:
- "Western blot protocol for protein detection"
- "RNA sequencing library preparation methods"
- "Statistical power analysis for clinical trials"
- "Machine learning model evaluation metrics"
预期响应格式:
研究统计:查找当前统计数据、调查结果和研究数据:
查询示例:
- "Prevalence of diabetes in US population 2024"
- "Global renewable energy adoption statistics"
- "COVID-19 vaccination rates by country"
- "AI adoption in healthcare industry survey"
预期响应格式:
引用查找:查找手稿中引用的相关论文和研究:
查询示例:
- "Foundational papers on transformer architecture"
- "Seminal works in quantum computing"
- "Key studies on climate change mitigation"
- "Landmark trials in cancer immunotherapy"
预期响应格式:
此技能具有基于查询复杂度的智能模型选择功能:
1. Sonar Pro Search (perplexity/sonar-pro-search)
2. Sonar Reasoning Pro (perplexity/sonar-reasoning-pro)
该技能使用以下指标自动检测查询复杂度:
推理关键词(触发 Sonar Reasoning Pro):
compare、contrast、analyze、analysis、evaluate、critiqueversus、vs、vs.、compared to、differences between、similaritiesmeta-analysis、systematic review、synthesis、integratemechanism、why、how does、how do、explain、relationship、causal relationship、underlying mechanismtheoretical framework、implications、interpret、reasoningcontroversy、conflicting、paradox、debate、reconcilepros and cons、advantages and disadvantages、trade-off、tradeoff、trade offsmultifaceted、complex interaction、critical analysis复杂度评分:
实际结果:即使只有一个强推理关键词(compare、explain、analyze 等)也会触发更强大的 Sonar Reasoning Pro 模型,确保在需要时获得深度分析。
查询分类示例:
✅ Sonar Pro Search(直接查询):
✅ Sonar Reasoning Pro(复杂分析):
您可以使用 force_model 参数强制使用特定模型:
# 强制使用 Sonar Pro Search 进行快速查询
research = ResearchLookup(force_model='pro')
# 强制使用 Sonar Reasoning Pro 进行深度分析
research = ResearchLookup(force_model='reasoning')
# 自动选择(默认)
research = ResearchLookup()
命令行用法:
# 强制使用 Sonar Pro Search
python research_lookup.py "your query" --force-model pro
# 强制使用 Sonar Reasoning Pro
python research_lookup.py "your query" --force-model reasoning
# 自动选择(无标志)
python research_lookup.py "your query"
此技能与 OpenRouter (openrouter.ai) 集成以访问 Perplexity 的 Sonar 模型:
模型规格:
perplexity/sonar-pro-search(快速查询)perplexity/sonar-reasoning-pro-online(深度分析)high 搜索上下文以获得更深入、更全面的研究结果API 要求:
OPENROUTER_API_KEY 环境变量)学术模式配置:
来源验证:该技能优先考虑:
引用标准:所有响应包括:
用于简单查询(Sonar Pro Search):
用于复杂分析(Sonar Reasoning Pro):
专业提示:自动选择已针对大多数用例进行优化。只有在有特定要求或知道查询需要比检测到的更深度推理时,才使用 force_model。
良好查询(将触发适当的模型):
不良查询:
推荐结构:
[主题] + [具体方面] + [时间范围] + [信息类型]
示例:
有效的后续查询:
此技能通过以下方式增强科学写作:
已知限制:
错误条件:
回退策略:
查询:"Recent advances in transformer attention mechanisms 2024"
选择的模型:Sonar Pro Search(直接查询)
响应包括:
查询:"Compare and contrast the advantages and limitations of transformer-based models versus traditional RNNs for sequence modeling"
选择的模型:Sonar Reasoning Pro(需要复杂分析)
响应包括:
查询:"Standard protocols for flow cytometry analysis"
选择的模型:Sonar Pro Search(协议查找)
响应包括:
查询:"Explain the underlying mechanism of how mRNA vaccines trigger immune responses and why they differ from traditional vaccines"
选择的模型:Sonar Reasoning Pro(需要因果推理)
响应包括:
查询:"Global AI adoption in healthcare statistics 2024"
选择的模型:Sonar Pro Search(数据查找)
响应包括:
Sonar Pro Search:
Sonar Reasoning Pro:
自动选择优势:
手动覆盖用例:
最佳实践:
负责任使用:
学术诚信:
除了 research-lookup 外,科学写作者还可以访问 WebSearch 用于:
何时使用哪个工具:
| 任务 | 工具 |
|---|---|
| 查找学术论文 | research-lookup |
| 文献搜索 | research-lookup |
| 深度分析/比较 | research-lookup (Sonar Reasoning Pro) |
| 查找 DOI/元数据 | WebSearch |
| 验证出版年份 | WebSearch |
| 查找期刊卷/页码 | WebSearch |
| 时事/新闻 | WebSearch |
| 非学术来源 | WebSearch |
此技能作为一个强大的研究助手,具有智能双模型选择功能:
无论您需要快速事实查找还是深度分析综合,此技能都能自动适应,为您的科学写作需求提供适当级别的研究支持。
每周安装次数
196
仓库
GitHub 星标数
22.6K
首次出现
2026 年 1 月 21 日
安全审计
安装于
opencode153
claude-code147
gemini-cli145
cursor139
codex135
github-copilot121
This skill enables real-time research information lookup using Perplexity's Sonar models through OpenRouter. It intelligently selects between Sonar Pro Search (fast, efficient lookup) and Sonar Reasoning Pro (deep analytical reasoning) based on query complexity. The skill provides access to current academic literature, recent studies, technical documentation, and general research information with proper citations and source attribution.
Use this skill when you need:
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Search Academic Literature : Query for recent papers, studies, and reviews in specific domains:
Query Examples:
- "Recent advances in CRISPR gene editing 2024"
- "Latest clinical trials for Alzheimer's disease treatment"
- "Machine learning applications in drug discovery systematic review"
- "Climate change impacts on biodiversity meta-analysis"
Expected Response Format :
Protocol and Method Lookups : Find detailed procedures, specifications, and methodologies:
Query Examples:
- "Western blot protocol for protein detection"
- "RNA sequencing library preparation methods"
- "Statistical power analysis for clinical trials"
- "Machine learning model evaluation metrics"
Expected Response Format :
Research Statistics : Look up current statistics, survey results, and research data:
Query Examples:
- "Prevalence of diabetes in US population 2024"
- "Global renewable energy adoption statistics"
- "COVID-19 vaccination rates by country"
- "AI adoption in healthcare industry survey"
Expected Response Format :
Citation Finding : Locate relevant papers and studies for citation in manuscripts:
Query Examples:
- "Foundational papers on transformer architecture"
- "Seminal works in quantum computing"
- "Key studies on climate change mitigation"
- "Landmark trials in cancer immunotherapy"
Expected Response Format :
This skill features intelligent model selection based on query complexity:
1. Sonar Pro Search (perplexity/sonar-pro-search)
2. Sonar Reasoning Pro (perplexity/sonar-reasoning-pro)
The skill automatically detects query complexity using these indicators:
Reasoning Keywords (triggers Sonar Reasoning Pro):
compare, contrast, analyze, analysis, evaluate, critiqueversus, vs, vs., compared to, differences between, similaritiesComplexity Scoring :
Practical Result : Even a single strong reasoning keyword (compare, explain, analyze, etc.) will trigger the more powerful Sonar Reasoning Pro model, ensuring you get deep analysis when needed.
Example Query Classification :
✅ Sonar Pro Search (straightforward lookup):
✅ Sonar Reasoning Pro (complex analysis):
You can force a specific model using the force_model parameter:
# Force Sonar Pro Search for fast lookup
research = ResearchLookup(force_model='pro')
# Force Sonar Reasoning Pro for deep analysis
research = ResearchLookup(force_model='reasoning')
# Automatic selection (default)
research = ResearchLookup()
Command-line usage:
# Force Sonar Pro Search
python research_lookup.py "your query" --force-model pro
# Force Sonar Reasoning Pro
python research_lookup.py "your query" --force-model reasoning
# Automatic (no flag)
python research_lookup.py "your query"
This skill integrates with OpenRouter (openrouter.ai) to access Perplexity's Sonar models:
Model Specifications :
perplexity/sonar-pro-search (fast lookup)perplexity/sonar-reasoning-pro-online (deep analysis)high search context for deeper, more comprehensive research resultsAPI Requirements :
OPENROUTER_API_KEY environment variable)Academic Mode Configuration :
Source Verification : The skill prioritizes:
Citation Standards : All responses include:
For Simple Lookups (Sonar Pro Search) :
For Complex Analysis (Sonar Reasoning Pro) :
Pro Tip : The automatic selection is optimized for most use cases. Only use force_model if you have specific requirements or know the query needs deeper reasoning than detected.
Good Queries (will trigger appropriate model):
Poor Queries :
Recommended Structure :
[Topic] + [Specific Aspect] + [Time Frame] + [Type of Information]
Examples :
Effective Follow-ups :
This skill enhances scientific writing by providing:
Known Limitations :
Error Conditions :
Fallback Strategies :
Query : "Recent advances in transformer attention mechanisms 2024"
Model Selected : Sonar Pro Search (straightforward lookup)
Response Includes :
Query : "Compare and contrast the advantages and limitations of transformer-based models versus traditional RNNs for sequence modeling"
Model Selected : Sonar Reasoning Pro (complex analysis required)
Response Includes :
Query : "Standard protocols for flow cytometry analysis"
Model Selected : Sonar Pro Search (protocol lookup)
Response Includes :
Query : "Explain the underlying mechanism of how mRNA vaccines trigger immune responses and why they differ from traditional vaccines"
Model Selected : Sonar Reasoning Pro (requires causal reasoning)
Response Includes :
Query : "Global AI adoption in healthcare statistics 2024"
Model Selected : Sonar Pro Search (data lookup)
Response Includes :
Sonar Pro Search :
Sonar Reasoning Pro :
Automatic Selection Benefits :
Manual Override Use Cases :
Best Practices :
Responsible Use :
Academic Integrity :
In addition to research-lookup, the scientific writer has access to WebSearch for:
When to use which tool:
| Task | Tool |
|---|---|
| Find academic papers | research-lookup |
| Literature search | research-lookup |
| Deep analysis/comparison | research-lookup (Sonar Reasoning Pro) |
| Look up DOI/metadata | WebSearch |
| Verify publication year | WebSearch |
| Find journal volume/pages | WebSearch |
| Current events/news | WebSearch |
| Non-scholarly sources | WebSearch |
This skill serves as a powerful research assistant with intelligent dual-model selection:
Whether you need quick fact-finding or deep analytical synthesis, this skill automatically adapts to deliver the right level of research support for your scientific writing needs.
Weekly Installs
196
Repository
GitHub Stars
22.6K
First Seen
Jan 21, 2026
Security Audits
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Installed on
opencode153
claude-code147
gemini-cli145
cursor139
codex135
github-copilot121
超能力技能使用指南:AI助手技能调用优先级与工作流程详解
41,800 周安装
meta-analysis, systematic review, synthesis, integratemechanism, why, how does, how do, explain, relationship, causal relationship, underlying mechanismtheoretical framework, implications, interpret, reasoningcontroversy, conflicting, paradox, debate, reconcilepros and cons, advantages and disadvantages, trade-off, tradeoff, trade offsmultifaceted, complex interaction, critical analysis