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Research Information Lookup

研究信息查询

Overview

概述

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.
该技能可通过OpenRouter利用Perplexity的Sonar模型实现实时研究信息查询。它会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）模型。该技能可获取最新学术文献、近期研究、技术文档及通用研究信息，并提供规范引用和来源归属。

When to Use This Skill

适用场景

Use this skill when you need:
  • Current Research Information: Latest studies, papers, and findings in a specific field
  • Literature Verification: Check facts, statistics, or claims against current research
  • Background Research: Gather context and supporting evidence for scientific writing
  • Citation Sources: Find relevant papers and studies to cite in manuscripts
  • Technical Documentation: Look up specifications, protocols, or methodologies
  • Recent Developments: Stay current with emerging trends and breakthroughs
  • Statistical Data: Find recent statistics, survey results, or research findings
  • Expert Opinions: Access insights from recent interviews, reviews, or commentary
在以下场景中使用该技能：
  • 最新研究信息：特定领域的最新研究、论文及发现
  • 文献验证：对照当前研究核实事实、统计数据或主张
  • 背景研究：为科学写作收集背景信息和支撑证据
  • 引用来源：查找可用于手稿引用的相关论文和研究
  • 技术文档：查询规格、协议或方法论
  • 最新进展：掌握新兴趋势和突破性成果
  • 统计数据：查找最新统计数据、调查结果或研究发现
  • 专家观点：获取近期访谈、综述或评论中的见解

Visual Enhancement with Scientific Schematics

科学示意图可视化增强

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:
  • Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
  • Simply describe your desired diagram in natural language
  • Nano Banana Pro will automatically generate, review, and refine the schematic
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:
bash
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
  • Create publication-quality images with proper formatting
  • Review and refine through multiple iterations
  • Ensure accessibility (colorblind-friendly, high contrast)
  • Save outputs in the figures/ directory
When to add schematics:
  • Research information flow diagrams
  • Query processing workflow illustrations
  • Model selection decision trees
  • System integration architecture diagrams
  • Information retrieval pipeline visualizations
  • Knowledge synthesis frameworks
  • Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
使用该技能创建文档时，务必考虑添加科学图表和示意图以提升视觉传达效果。
如果你的文档中尚未包含示意图或图表：
  • 使用scientific-schematics技能生成AI驱动的出版级图表
  • 只需用自然语言描述你想要的图表
  • Nano Banana Pro会自动生成、审核并优化示意图
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、架构或关系。
生成示意图的方法：
bash
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
AI会自动：
  • 创建格式规范的出版级图片
  • 通过多轮迭代审核和优化
  • 确保可访问性（色盲友好、高对比度）
  • 将输出保存至figures/目录
添加示意图的场景：
  • 研究信息流图
  • 查询处理工作流示意图
  • 模型选择决策树
  • 系统集成架构图
  • 信息检索管道可视化
  • 知识合成框架
  • 任何需要可视化的复杂概念
如需创建示意图的详细指南，请参考scientific-schematics技能文档。

Core Capabilities

核心能力

1. Academic Research Queries

1. 学术研究查询

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:
  • Summary of key findings from recent literature
  • Citation of 3-5 most relevant papers with authors, titles, journals, and years
  • Key statistics or findings highlighted
  • Identification of research gaps or controversies
  • Links to full papers when available
学术文献搜索：查询特定领域的近期论文、研究及综述：
查询示例：
- "2024年CRISPR基因编辑的最新进展"
- "阿尔茨海默病治疗的最新临床试验"
- "机器学习在药物发现中的应用系统综述"
- "气候变化对生物多样性影响的元分析"
预期响应格式：
  • 近期文献关键发现摘要
  • 3-5篇最相关论文的引用信息，包含作者、标题、期刊及年份
  • 重点突出的关键统计数据或发现
  • 研究空白或争议点识别
  • 可获取的全文链接

2. Technical and Methodological Information

2. 技术与方法信息

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:
  • Step-by-step procedures or protocols
  • Required materials and equipment
  • Critical parameters and considerations
  • Troubleshooting common issues
  • References to standard protocols or seminal papers
协议与方法查询：查找详细流程、规格和方法论：
查询示例：
- "蛋白质检测的Western Blot实验协议"
- "RNA测序文库制备方法"
- "临床试验的统计功效分析"
- "机器学习模型评估指标"
预期响应格式：
  • 分步流程或协议
  • 所需材料与设备
  • 关键参数和注意事项
  • 常见问题排查
  • 标准协议或开创性论文的参考资料

3. Statistical and Data Information

3. 统计与数据信息

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:
  • Current statistics with dates and sources
  • Methodology of data collection
  • Confidence intervals or margins of error when available
  • Comparison with previous years or benchmarks
  • Citations to original surveys or studies
研究统计数据：查询最新统计数据、调查结果和研究数据：
查询示例：
- "2024年美国糖尿病患病率"
- "全球可再生能源采用统计数据"
- "各国COVID-19疫苗接种率"
- "医疗行业AI采用情况调查"
预期响应格式：
  • 带日期和来源的最新统计数据
  • 数据收集方法论
  • 可用时提供置信区间或误差范围
  • 与往年数据或基准的对比
  • 原始调查或研究的引用信息

4. Citation and Reference Assistance

4. 引用与参考辅助

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:
  • 5-10 most influential or relevant papers
  • Complete citation information (authors, title, journal, year, DOI)
  • Brief description of each paper's contribution
  • Citation impact metrics when available (h-index, citation count)
  • Journal impact factors and rankings
引用查找：定位可用于手稿引用的相关论文和研究：
查询示例：
- "Transformer架构的奠基性论文"
- "量子计算的开创性成果"
- "气候变化减缓的关键研究"
- "癌症免疫治疗的里程碑试验"
预期响应格式：
  • 5-10篇最具影响力或相关性的论文
  • 完整引用信息（作者、标题、期刊、年份、DOI）
  • 每篇论文贡献的简要描述
  • 可用时提供引用影响指标（h指数、引用次数）
  • 期刊影响因子和排名

Automatic Model Selection

自动模型选择

This skill features intelligent model selection based on query complexity:
该技能具备基于查询复杂度的智能模型选择功能：

Model Types

模型类型

1. Sonar Pro Search (
perplexity/sonar-pro-search
)
  • Use Case: Straightforward information lookup
  • Best For:
    • Simple fact-finding queries
    • Recent publication searches
    • Basic protocol lookups
    • Statistical data retrieval
  • Speed: Fast responses
  • Cost: Lower cost per query
2. Sonar Reasoning Pro (
perplexity/sonar-reasoning-pro
)
  • Use Case: Complex analytical queries requiring deep reasoning
  • Best For:
    • Comparative analysis ("compare X vs Y")
    • Synthesis of multiple studies
    • Evaluating trade-offs or controversies
    • Explaining mechanisms or relationships
    • Critical analysis and interpretation
  • Speed: Slower but more thorough
  • Cost: Higher cost per query, but provides deeper insights
1. Sonar Pro Search (
perplexity/sonar-pro-search
)
  • 适用场景：直接信息查询
  • 最佳用途：
    • 简单事实查询
    • 最新出版物搜索
    • 基础协议查询
    • 统计数据检索
  • 速度：响应快速
  • 成本：单次查询成本较低
2. Sonar Reasoning Pro (
perplexity/sonar-reasoning-pro
)
  • 适用场景：需要深度推理的复杂分析查询
  • 最佳用途：
    • 对比分析（"compare X vs Y"）
    • 多项研究综合
    • 权衡评估或争议分析
    • 机制或关系解释
    • 批判性分析与解读
  • 速度：响应较慢但更全面
  • 成本：单次查询成本较高，但提供更深入的见解

Complexity Assessment

复杂度评估

The skill automatically detects query complexity using these indicators:
Reasoning Keywords (triggers Sonar Reasoning Pro):
  • Analytical: 
    compare
    , 
    contrast
    , 
    analyze
    , 
    analysis
    , 
    evaluate
    , 
    critique
  • Comparative: 
    versus
    , 
    vs
    , 
    vs.
    , 
    compared to
    , 
    differences between
    , 
    similarities
  • Synthesis: 
    meta-analysis
    , 
    systematic review
    , 
    synthesis
    , 
    integrate
  • Causal: 
    mechanism
    , 
    why
    , 
    how does
    , 
    how do
    , 
    explain
    , 
    relationship
    , 
    causal relationship
    , 
    underlying mechanism
  • Theoretical: 
    theoretical framework
    , 
    implications
    , 
    interpret
    , 
    reasoning
  • Debate: 
    controversy
    , 
    conflicting
    , 
    paradox
    , 
    debate
    , 
    reconcile
  • Trade-offs: 
    pros and cons
    , 
    advantages and disadvantages
    , 
    trade-off
    , 
    tradeoff
    , 
    trade offs
  • Complexity: 
    multifaceted
    , 
    complex interaction
    , 
    critical analysis
Complexity Scoring:
  • Reasoning keywords: 3 points each (heavily weighted)
  • Multiple questions: 2 points per question mark
  • Complex sentence structures: 1.5 points per clause indicator (and, or, but, however, whereas, although)
  • Very long queries: 1 point if >150 characters
  • Threshold: Queries scoring ≥3 points trigger Sonar Reasoning Pro
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):
  • "Recent advances in CRISPR gene editing 2024"
  • "Prevalence of diabetes in US population"
  • "Western blot protocol for protein detection"
✅ Sonar Reasoning Pro (complex analysis):
  • "Compare and contrast mRNA vaccines vs traditional vaccines for cancer treatment"
  • "Explain the mechanism underlying the relationship between gut microbiome and depression"
  • "Analyze the controversy surrounding AI in medical diagnosis and evaluate trade-offs"
该技能通过以下指标自动检测查询复杂度：
推理关键词（触发Sonar Reasoning Pro）：
  • 分析类：
    compare
    , 
    contrast
    , 
    analyze
    , 
    analysis
    , 
    evaluate
    , 
    critique
  • 对比类：
    versus
    , 
    vs
    , 
    vs.
    , 
    compared to
    , 
    differences between
    , 
    similarities
  • 综合类：
    meta-analysis
    , 
    systematic review
    , 
    synthesis
    , 
    integrate
  • 因果类：
    mechanism
    , 
    why
    , 
    how does
    , 
    how do
    , 
    explain
    , 
    relationship
    , 
    causal relationship
    , 
    underlying mechanism
  • 理论类：
    theoretical framework
    , 
    implications
    , 
    interpret
    , 
    reasoning
  • 争议类：
    controversy
    , 
    conflicting
    , 
    paradox
    , 
    debate
    , 
    reconcile
  • 权衡类：
    pros and cons
    , 
    advantages and disadvantages
    , 
    trade-off
    , 
    tradeoff
    , 
    trade offs
  • 复杂类：
    multifaceted
    , 
    complex interaction
    , 
    critical analysis
复杂度评分：
  • 推理关键词：每个3分（权重较高）
  • 多个问题：每个问号2分
  • 复杂句式结构：每个从句标记（and, or, but, however, whereas, although）1.5分
  • 超长查询：超过150字符得1分
  • 阈值：评分≥3分触发Sonar Reasoning Pro
实际效果：即使单个强推理关键词（如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结果。
查询分类示例：
✅ Sonar Pro Search（直接查询）：
  • "2024年CRISPR基因编辑的最新进展"
  • "美国糖尿病患病率"
  • "蛋白质检测的Western Blot实验协议"
✅ Sonar Reasoning Pro（复杂分析）：
  • "对比mRNA疫苗与传统疫苗在癌症治疗中的效果"
  • "解释肠道微生物组与抑郁症之间关系的潜在机制"
  • "分析AI在医学诊断中的争议并评估利弊"

Manual Override

手动覆盖

You can force a specific model using the 
force_model
parameter:
python
undefined
你可以使用
force_model
参数强制指定特定模型：
python
undefined

Force Sonar Pro Search for fast lookup

强制使用Sonar Pro Search以快速查询

research = ResearchLookup(force_model='pro')
research = ResearchLookup(force_model='pro')

Force Sonar Reasoning Pro for deep analysis

强制使用Sonar Reasoning Pro以进行深度分析

research = ResearchLookup(force_model='reasoning')
research = ResearchLookup(force_model='reasoning')

Automatic selection (default)

自动选择（默认）

research = ResearchLookup()

Command-line usage:
```bash
research = ResearchLookup()

命令行用法：
```bash

Force Sonar Pro Search

强制使用Sonar Pro Search

python research_lookup.py "your query" --force-model pro
python research_lookup.py "your query" --force-model pro

Force Sonar Reasoning Pro

强制使用Sonar Reasoning Pro

python research_lookup.py "your query" --force-model reasoning
python research_lookup.py "your query" --force-model reasoning

Automatic (no flag)

自动选择（无标识）

python research_lookup.py "your query"
undefined
python research_lookup.py "your query"
undefined

Technical Integration

技术集成

OpenRouter API Configuration

OpenRouter API配置

This skill integrates with OpenRouter (openrouter.ai) to access Perplexity's Sonar models:
Model Specifications:
  • Models: 
    • perplexity/sonar-pro-search
      (fast lookup)
    • perplexity/sonar-reasoning-pro-online
      (deep analysis)
  • Search Mode: Academic/scholarly mode (prioritizes peer-reviewed sources)
  • Search Context: Always uses 
    high
    search context for deeper, more comprehensive research results
  • Context Window: 200K+ tokens for comprehensive research
  • Capabilities: Academic paper search, citation generation, scholarly analysis
  • Output: Rich responses with citations and source links from academic databases
API Requirements:
  • OpenRouter API key (set as 
    OPENROUTER_API_KEY
    environment variable)
  • Account with sufficient credits for research queries
  • Proper attribution and citation of sources
Academic Mode Configuration:
  • System message configured to prioritize scholarly sources
  • Search focused on peer-reviewed journals and academic publications
  • Enhanced citation extraction for academic references
  • Preference for recent academic literature (2020-2024)
  • Direct access to academic databases and repositories
该技能与OpenRouter（openrouter.ai）集成，以访问Perplexity的Sonar模型：
模型规格：
  • 模型： 
    • perplexity/sonar-pro-search
      （快速查询）
    • perplexity/sonar-reasoning-pro-online
      （深度分析）
  • 搜索模式：学术/科研模式（优先同行评审来源）
  • 搜索上下文：始终使用
    high
    搜索上下文以获得更深入、全面的研究结果
  • 上下文窗口：200K+ tokens，支持全面研究
  • 能力：学术论文搜索、引用生成、学术分析
  • 输出：包含学术数据库引用和来源链接的丰富响应
API要求：
  • OpenRouter API密钥（设置为
    OPENROUTER_API_KEY
    环境变量）
  • 拥有足够查询额度的账户
  • 对来源进行适当归属和引用
学术模式配置：
  • 系统消息配置为优先学术来源
  • 搜索聚焦于同行评审期刊和学术出版物
  • 增强学术引用的提取功能
  • 偏好近期学术文献（2020-2024）
  • 直接访问学术数据库和知识库

Response Quality and Reliability

响应质量与可靠性

Source Verification: The skill prioritizes:
  • Peer-reviewed academic papers and journals
  • Reputable institutional sources (universities, government agencies, NGOs)
  • Recent publications (within last 2-3 years preferred)
  • High-impact journals and conferences
  • Primary research over secondary sources
Citation Standards: All responses include:
  • Complete bibliographic information
  • DOI or stable URLs when available
  • Access dates for web sources
  • Clear attribution of direct quotes or data
来源验证：该技能优先选择：
  • 同行评审学术论文和期刊
  • 知名机构来源（大学、政府机构、非政府组织）
  • 近期出版物（优先过去2-3年）
  • 高影响力期刊和会议
  • 原始研究而非二次来源
引用标准：所有响应包含：
  • 完整书目信息
  • 可用时提供DOI或稳定URL
  • 网络来源的访问日期
  • 直接引用或数据的明确归属

Query Best Practices

查询最佳实践

1. Model Selection Strategy

1. 模型选择策略

For Simple Lookups (Sonar Pro Search):
  • Recent papers on a specific topic
  • Statistical data or prevalence rates
  • Standard protocols or methodologies
  • Citation finding for specific papers
  • Factual information retrieval
For Complex Analysis (Sonar Reasoning Pro):
  • Comparative studies and synthesis
  • Mechanism explanations
  • Controversy evaluation
  • Trade-off analysis
  • Theoretical frameworks
  • Multi-faceted relationships
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.
简单查询（Sonar Pro Search）：
  • 特定主题的近期论文
  • 统计数据或患病率
  • 标准协议或方法论
  • 特定论文的引用查找
  • 事实信息检索
复杂分析（Sonar Reasoning Pro）：
  • 对比研究与综合
  • 机制解释
  • 争议评估
  • 利弊分析
  • 理论框架
  • 多维度关系
专业提示：自动选择功能已针对大多数使用场景优化。仅在有特定需求或明确知道查询需要更深入推理时使用
force_model
。

2. Specific and Focused Queries

2. 具体且聚焦的查询

Good Queries (will trigger appropriate model):
  • "Randomized controlled trials of mRNA vaccines for cancer treatment 2023-2024" → Sonar Pro Search
  • "Compare the efficacy and safety of mRNA vaccines vs traditional vaccines for cancer treatment" → Sonar Reasoning Pro
  • "Explain the mechanism by which CRISPR off-target effects occur and strategies to minimize them" → Sonar Reasoning Pro
Poor Queries:
  • "Tell me about AI" (too broad)
  • "Cancer research" (lacks specificity)
  • "Latest news" (too vague)
优质查询（将触发合适模型）：
  • "2023-2024年mRNA疫苗治疗癌症的随机对照试验" → Sonar Pro Search
  • "对比mRNA疫苗与传统疫苗在癌症治疗中的疗效与安全性" → Sonar Reasoning Pro
  • "解释CRISPR脱靶效应的发生机制及最小化策略" → Sonar Reasoning Pro
劣质查询：
  • "告诉我关于AI的信息"（过于宽泛）
  • "癌症研究"（缺乏针对性）
  • "最新新闻"（过于模糊）

3. Structured Query Format

3. 结构化查询格式

Recommended Structure:
[Topic] + [Specific Aspect] + [Time Frame] + [Type of Information]
Examples:
  • "CRISPR gene editing + off-target effects + 2024 + clinical trials"
  • "Quantum computing + error correction + recent advances + review papers"
  • "Renewable energy + solar efficiency + 2023-2024 + statistical data"
推荐结构：
[主题] + [具体方面] + [时间范围] + [信息类型]
示例：
  • "CRISPR基因编辑 + 脱靶效应 + 2024 + 临床试验"
  • "量子计算 + 错误校正 + 最新进展 + 综述论文"
  • "可再生能源 + 太阳能效率 + 2023-2024 + 统计数据"

4. Follow-up Queries

4. 跟进查询

Effective Follow-ups:
  • "Show me the full citation for the Smith et al. 2024 paper"
  • "What are the limitations of this methodology?"
  • "Find similar studies using different approaches"
  • "What controversies exist in this research area?"
有效跟进查询：
  • "展示Smith等人2024年论文的完整引用信息"
  • "该方法论的局限性是什么？"
  • "查找使用不同方法的类似研究"
  • "该研究领域存在哪些争议？"

Integration with Scientific Writing

与科学写作的集成

This skill enhances scientific writing by providing:
  1. Literature Review Support: Gather current research for introduction and discussion sections
  2. Methods Validation: Verify protocols and procedures against current standards
  3. Results Contextualization: Compare findings with recent similar studies
  4. Discussion Enhancement: Support arguments with latest evidence
  5. Citation Management: Provide properly formatted citations in multiple styles
该技能通过以下方式增强科学写作：
  1. 文献综述支持：为引言和讨论部分收集最新研究
  2. 方法验证：对照当前标准核实协议和流程
  3. 结果语境化：将发现与近期类似研究对比
  4. 讨论增强：用最新证据支撑论点
  5. 引用管理：提供多种格式的规范引用

Error Handling and Limitations

错误处理与局限性

Known Limitations:
  • Information cutoff: Responses limited to training data (typically 2023-2024)
  • Paywall content: May not access full text behind paywalls
  • Emerging research: May miss very recent papers not yet indexed
  • Specialized databases: Cannot access proprietary or restricted databases
Error Conditions:
  • API rate limits or quota exceeded
  • Network connectivity issues
  • Malformed or ambiguous queries
  • Model unavailability or maintenance
Fallback Strategies:
  • Rephrase queries for better clarity
  • Break complex queries into simpler components
  • Use broader time frames if recent data unavailable
  • Cross-reference with multiple query variations
已知局限性：
  • 信息截止：响应限于训练数据（通常为2023-2024年）
  • 付费墙内容：可能无法访问付费墙后的全文
  • 新兴研究：可能遗漏尚未被索引的最新论文
  • 专业数据库：无法访问专有或受限数据库
错误场景：
  • API速率限制或配额耗尽
  • 网络连接问题
  • 查询格式错误或模糊
  • 模型不可用或维护中
** fallback策略**：
  • 重新表述查询以提高清晰度
  • 将复杂查询拆分为更简单的部分
  • 若近期数据不可用，使用更宽泛的时间范围
  • 用多种查询变体交叉验证

Usage Examples

使用示例

Example 1: Simple Literature Search (Sonar Pro Search)

示例1：简单文献搜索（Sonar Pro Search）

Query: "Recent advances in transformer attention mechanisms 2024"
Model Selected: Sonar Pro Search (straightforward lookup)
Response Includes:
  • Summary of 5 key papers from 2024
  • Complete citations with DOIs
  • Key innovations and improvements
  • Performance benchmarks
  • Future research directions
查询："2024年Transformer注意力机制的最新进展"
选择模型：Sonar Pro Search（直接查询）
响应包含：
  • 2024年5篇关键论文的摘要
  • 带DOI的完整引用信息
  • 关键创新与改进
  • 性能基准
  • 未来研究方向

Example 2: Comparative Analysis (Sonar Reasoning Pro)

示例2：对比分析（Sonar Reasoning Pro）

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:
  • Detailed comparison across multiple dimensions
  • Analysis of architectural differences
  • Trade-offs in computational efficiency vs performance
  • Use case recommendations
  • Synthesis of evidence from multiple studies
  • Discussion of ongoing debates in the field
查询："对比Transformer模型与传统RNN在序列建模中的优势与局限性"
选择模型：Sonar Reasoning Pro（需要复杂分析）
响应包含：
  • 多维度详细对比
  • 架构差异分析
  • 计算效率与性能的权衡
  • 适用场景建议
  • 多项研究证据综合
  • 领域内持续争议的讨论

Example 3: Method Verification (Sonar Pro Search)

示例3：方法验证（Sonar Pro Search）

Query: "Standard protocols for flow cytometry analysis"
Model Selected: Sonar Pro Search (protocol lookup)
Response Includes:
  • Step-by-step protocol from recent review
  • Required controls and calibrations
  • Common pitfalls and troubleshooting
  • Reference to definitive methodology paper
  • Alternative approaches with pros/cons
查询："流式细胞术分析的标准协议"
选择模型：Sonar Pro Search（协议查询）
响应包含：
  • 近期综述中的分步协议
  • 所需对照与校准
  • 常见陷阱与排查方法
  • 权威方法论论文参考
  • 优缺点并存的替代方法

Example 4: Mechanism Explanation (Sonar Reasoning Pro)

示例4：机制解释（Sonar Reasoning Pro）

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:
  • Detailed mechanistic explanation
  • Step-by-step biological processes
  • Comparative analysis with traditional vaccines
  • Molecular-level interactions
  • Integration of immunology and pharmacology concepts
  • Evidence from recent research
查询："解释mRNA疫苗触发免疫反应的潜在机制，以及它们与传统疫苗的区别"
选择模型：Sonar Reasoning Pro（需要因果推理）
响应包含：
  • 详细机制解释
  • 分步生物过程
  • 与传统疫苗的对比分析
  • 分子层面的相互作用
  • 免疫学与药理学概念整合
  • 近期研究证据

Example 5: Statistical Data (Sonar Pro Search)

示例5：统计数据（Sonar Pro Search）

Query: "Global AI adoption in healthcare statistics 2024"
Model Selected: Sonar Pro Search (data lookup)
Response Includes:
  • Current adoption rates by region
  • Market size and growth projections
  • Survey methodology and sample size
  • Comparison with previous years
  • Citations to market research reports
查询："2024年全球医疗行业AI采用统计数据"
选择模型：Sonar Pro Search（数据查询）
响应包含：
  • 各地区当前采用率
  • 市场规模与增长预测
  • 调查方法与样本量
  • 与往年数据对比
  • 市场研究报告引用

Performance and Cost Considerations

性能与成本考量

Response Times

响应时间

Sonar Pro Search:
  • Typical response time: 5-15 seconds
  • Best for rapid information gathering
  • Suitable for batch queries
Sonar Reasoning Pro:
  • Typical response time: 15-45 seconds
  • Worth the wait for complex analytical queries
  • Provides more thorough reasoning and synthesis
Sonar Pro Search：
  • 典型响应时间：5-15秒
  • 适合快速信息收集
  • 支持批量查询
Sonar Reasoning Pro：
  • 典型响应时间：15-45秒
  • 复杂分析查询值得等待
  • 提供更全面的推理与综合

Cost Optimization

成本优化

Automatic Selection Benefits:
  • Saves costs by using Sonar Pro Search for straightforward queries
  • Reserves Sonar Reasoning Pro for queries that truly benefit from deeper analysis
  • Optimizes the balance between cost and quality
Manual Override Use Cases:
  • Force Sonar Pro Search when budget is constrained and speed is priority
  • Force Sonar Reasoning Pro when working on critical research requiring maximum depth
  • Use for specific sections of papers (e.g., Pro Search for methods, Reasoning for discussion)
Best Practices:
  1. Trust the automatic selection for most use cases
  2. Review query results - if Sonar Pro Search doesn't provide sufficient depth, rephrase with reasoning keywords
  3. Use batch queries strategically - combine simple lookups to minimize total query count
  4. For literature reviews, start with Sonar Pro Search for breadth, then use Sonar Reasoning Pro for synthesis
自动选择优势：
  • 通过对直接查询使用Sonar Pro Search节省成本
  • 仅将Sonar Reasoning Pro用于真正需要深度分析的查询
  • 优化成本与质量的平衡
手动覆盖适用场景：
  • 预算有限且优先速度时，强制使用Sonar Pro Search
  • 进行需要最大深度的关键研究时，强制使用Sonar Reasoning Pro
  • 针对论文特定部分使用（如方法部分用Pro Search，讨论部分用Reasoning）
最佳实践：
  1. 大多数场景下信任自动选择功能
  2. 查看查询结果——如果Sonar Pro Search提供的深度不足，添加推理关键词重新表述查询
  3. 战略性使用批量查询——合并简单查询以减少总查询次数
  4. 文献综述时，先用Sonar Pro Search获取广度，再用Sonar Reasoning Pro进行综合

Security and Ethical Considerations

安全与伦理考量

Responsible Use:
  • Verify all information against primary sources when possible
  • Clearly attribute all data and quotes to original sources
  • Avoid presenting AI-generated summaries as original research
  • Respect copyright and licensing restrictions
  • Use for research assistance, not to bypass paywalls or subscriptions
Academic Integrity:
  • Always cite original sources, not the AI tool
  • Use as a starting point for literature searches
  • Follow institutional guidelines for AI tool usage
  • Maintain transparency about research methods
负责任使用：
  • 尽可能对照原始来源验证所有信息
  • 明确将所有数据和引用归属到原始来源
  • 避免将AI生成的摘要作为原创研究呈现
  • 尊重版权和许可限制
  • 用于研究辅助，而非绕过付费墙或订阅
学术诚信：
  • 始终引用原始来源，而非AI工具
  • 将其作为文献搜索的起点
  • 遵循机构对AI工具使用的指导方针
  • 保持研究方法的透明度

Complementary Tools

互补工具

In addition to research-lookup, the scientific writer has access to WebSearch for:
  • Quick metadata verification: Look up DOIs, publication years, journal names, volume/page numbers
  • Non-academic sources: News, blogs, technical documentation, current events
  • General information: Company info, product details, current statistics
  • Cross-referencing: Verify citation details found through research-lookup
When to use which tool:
TaskTool
Find academic papersresearch-lookup
Literature searchresearch-lookup
Deep analysis/comparisonresearch-lookup (Sonar Reasoning Pro)
Look up DOI/metadataWebSearch
Verify publication yearWebSearch
Find journal volume/pagesWebSearch
Current events/newsWebSearch
Non-scholarly sourcesWebSearch
除research-lookup外，科学写作者还可使用WebSearch完成以下任务：
  • 快速元数据验证：查询DOI、出版年份、期刊名称、卷/页码
  • 非学术来源：新闻、博客、技术文档、当前事件
  • 通用信息：公司信息、产品详情、当前统计数据
  • 交叉引用：验证通过research-lookup找到的引用细节
工具选择指南：
任务工具
查找学术论文research-lookup
文献搜索research-lookup
深度分析/对比research-lookup（Sonar Reasoning Pro）
查询DOI/元数据WebSearch
核实出版年份WebSearch
查找期刊卷/页码WebSearch
当前事件/新闻WebSearch
非学术来源WebSearch

Summary

总结

This skill serves as a powerful research assistant with intelligent dual-model selection:
  • Automatic Intelligence: Analyzes query complexity and selects the optimal model (Sonar Pro Search or Sonar Reasoning Pro)
  • Cost-Effective: Uses faster, cheaper Sonar Pro Search for straightforward lookups
  • Deep Analysis: Automatically engages Sonar Reasoning Pro for complex comparative, analytical, and theoretical queries
  • Flexible Control: Manual override available when you know exactly what level of analysis you need
  • Academic Focus: Both models configured to prioritize peer-reviewed sources and scholarly literature
  • Complementary WebSearch: Use alongside WebSearch for metadata verification and non-academic sources
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.
该技能是具备智能双模型选择功能的强大研究助手：
  • 自动智能：分析查询复杂度并选择最优模型（Sonar Pro Search或Sonar Reasoning Pro）
  • 成本高效：对直接查询使用更快、更便宜的Sonar Pro Search
  • 深度分析：自动调用Sonar Reasoning Pro处理复杂的对比、分析和理论查询
  • 灵活控制：当你明确知道所需分析深度时，可使用手动覆盖功能
  • 学术聚焦：两个模型均配置为优先同行评审来源和学术文献
  • 互补WebSearch：与WebSearch配合使用以验证元数据和获取非学术来源
无论你需要快速事实查找还是深度分析综合，该技能都会自动适配，为你的科学写作需求提供合适的研究支持。